7. Advanced RAG Techniques

"The future of RAG is not retrieval alone, but adaptive intelligence that knows when to retrieve, how to reason, and what to optimize." — Advanced RAG Principle

This chapter covers frontier RAG techniques that solve specific production challenges: modular architectures (dynamic routing, iterative retrieval), knowledge graph integration (GraphRAG), agentic systems (Self-RAG, Corrective RAG), fine-tuning fusion (domain adaptation, RAFT), and performance optimization (caching, quantization).

7.1 RAG Challenges & Decision Matrix

7.1.1 The Production Gap

Basic RAG systems suffer from fundamental limitations in production environments:

The Six Fundamental Challenges:

Challenge	Symptom	Root Cause	Impact
Hallucination	Answer invents facts not in documents	LLM relies on pre-training instead of context	Loss of trust, legal risk
Poor Retrieval Accuracy	Retrieved documents miss key information	Single-hop vector search insufficient	Incomplete answers
High Latency & Cost	Slow responses, expensive API calls	All queries get full processing	Poor UX, budget overruns
Limited Reasoning Depth	Cannot connect multiple facts	No multi-hop inference	Shallow answers
Domain Adaptation Gap	General embeddings miss domain jargon	Models trained on general corpus	Poor retrieval in specialized fields
Rigid Linear Pipeline	Simple and complex queries treated equally	Fixed retrieve-then-generate flow	Inefficiency, wasted compute

7.1.2 Decision Matrix

Which technique solves which problem?

Comprehensive Decision Matrix:

Problem	Primary Solution	Production Readiness	Complexity	Expected Improvement	Use Case
Static pipeline inefficiency	Modular RAG	High	Medium	30-40% cost reduction	Mixed query complexity
Multi-hop reasoning failure	GraphRAG	Very High	High	2-3x MRR improvement	Complex relationships
Hallucination	Agentic RAG	Medium-High	High	15-20% accuracy gain	Accuracy-critical apps
Domain knowledge gap	RAG + Fine-tuning	High	Very High	20-30% domain QA gain	Specialized domains
Performance/cost issues	Optimization	Very High	Low-Medium	90% latency reduction	All production systems
Low retrieval precision	Hybrid (Modular + Graph)	Medium	Very High	Combined benefits	Enterprise-grade systems

2025 Insight: Technique Combination

Research shows that combining techniques yields better results than any single approach:

Modular RAG + GraphRAG → Adaptive + relational reasoning
Agentic RAG + Fine-tuning → Self-reflective + domain expertise
Optimization + Any technique → Production-ready performance

7.2 Modular RAG - From Linear to Adaptive

7.2.1 Paradigm Shift: Linear vs Modular

Linear RAG treats all queries identically:

Query → Embed → Retrieve → Generate
One-size-fits-all pipeline
Wastes compute on simple queries

Modular RAG adapts to query characteristics:

Query → Analyze → Route → Specialized Processing → Generate
Different paths for different needs
Efficient resource allocation

Key Differences:

Aspect	Linear RAG	Modular RAG
Pipeline	Fixed retrieve-then-generate	Dynamic routing based on query
Query Analysis	None	Classification before routing
Retrieval Strategy	Always vector search	Vector, Graph, Web, or None
Compute Efficiency	Low (all queries get full processing)	High (tailored processing)
Latency	Uniform (often slow)	Variable (fast for simple queries)
Cost	High (unnecessary operations)	Optimized (minimal operations)

7.2.2 Dynamic Routing (Semantic Router)

Concept: Classify queries and route to specialized processing paths.

Implementation in Java/Spring Boot:

// Semantic Router Service
@Service
public class SemanticRouter {

    private final ChatModel llm;
    private final VectorStore vectorStore;
    private final GraphStore graphStore;
    private final WebSearchService webSearch;

    public enum Route {
        VECTOR_DB,      // Factual questions needing document retrieval
        GRAPH_DB,       // Relational questions requiring multi-hop reasoning
        WEB_SEARCH,    // Questions needing recent information
        LLM_DIRECT     // Common sense or general knowledge
    }

    public QueryRouteResult routeQuery(String query) {
        // Step 1: Classify query using LLM
        String classificationPrompt = """
            Classify this query into one of these categories:

            Query: %s

            Categories:
            1. VECTOR_DB - Factual questions about specific documents, procedures, or facts
            2. GRAPH_DB - Questions requiring multi-hop reasoning, relationships, or connections
            3. WEB_SEARCH - Questions about recent events, current prices, or time-sensitive data
            4. LLM_DIRECT - Common sense questions, general knowledge, or conversational queries

            Output only the category name.
            """.formatted(query);

        String category = llm.call(classificationPrompt);

        Route route = Route.valueOf(category);

        // Step 2: Extract confidence score
        double confidence = extractConfidence(query, route);

        return new QueryRouteResult(route, confidence);
    }

    public String processQuery(String query) {
        QueryRouteResult routing = routeQuery(query);

        return switch (routing.route()) {
            case VECTOR_DB -> handleVectorRetrieval(query);
            case GRAPH_DB -> handleGraphRetrieval(query);
            case WEB_SEARCH -> handleWebSearch(query);
            case LLM_DIRECT -> handleDirectLLM(query);
        };
    }

    private String handleVectorRetrieval(String query) {
        // Vector similarity search
        List<Document> docs = vectorStore.similaritySearch(
            SearchRequest.query(query).withTopK(5)
        );

        // Generate answer with retrieved context
        return generateWithRAG(query, docs);
    }

    private String handleGraphRetrieval(String query) {
        // Extract entities from query
        List<String> entities = extractEntities(query);

        // Graph traversal for multi-hop reasoning
        List<GraphPath> paths = graphStore.findPaths(
            entities,
            maxDepth = 3,
            maxPaths = 10
        );

        // Convert paths to context
        String context = formatGraphContext(paths);

        // Generate answer with graph context
        return generateWithRAG(query, context);
    }

    private String handleWebSearch(String query) {
        // Live web search for recent information
        List<WebResult> results = webSearch.search(query, maxResults = 5);

        String context = formatWebResults(results);

        return generateWithRAG(query, context);
    }

    private String handleDirectLLM(String query) {
        // Direct LLM call without retrieval
        String prompt = "Answer this question directly: %s".formatted(query);
        return llm.call(prompt);
    }

    private double extractConfidence(String query, Route route) {
        // Use LLM to score routing confidence
        String confidencePrompt = """
            Query: %s
            Suggested Route: %s

            Score your confidence in this routing decision (0.0 to 1.0):
            - 1.0: Very confident this is the correct route
            - 0.5: Moderately confident
            - 0.0: Not confident at all

            Output only the score.
            """.formatted(query, route);

        String response = llm.call(confidencePrompt);
        return Double.parseDouble(response.trim());
    }
}

// Route Result Record
record QueryRouteResult(SemanticRouter.Route route, double confidence) {
    public boolean isConfident() {
        return confidence >= 0.7;
    }
}

Routing Strategies Table:

Query Pattern	Route	Confidence Threshold	Processing
"How do I configure..."	VECTOR_DB	> 0.7	Vector search + RAG
"What is the relationship between..."	GRAPH_DB	> 0.7	Graph traversal + RAG
"What is the current price of..."	WEB_SEARCH	> 0.7	Web search + RAG
"Tell me a joke"	LLM_DIRECT	> 0.7	Direct LLM response
Ambiguous / Low confidence	Fallback	< 0.7	Multiple routes + ensemble

Tools & Libraries:

Tool	Language	Features	Integration
LangRouter	Python	Semantic routing, confidence scoring	LangChain
Semantic Router	Python	Fast semantic routing	LlamaIndex, LangChain
Spring AI	Java	Native routing support	Spring Boot
Custom Implementation	Any	Full control	Any framework

7.2.3 Iterative Retrieval (ITER-RETGEN)

Problem: Single retrieval is often insufficient for complex, multi-part questions.

Solution: ITER-RETGEN (Iterative Retrieval-Generation) — Multi-round retrieval with answer refinement.

Algorithm:

Generate initial answer with available context
Identify information gaps (what's missing?)
Generate new query based on gaps
Retrieve additional documents
Refine and complete answer
Repeat until satisfied

Implementation in Java:

@Service
public class IterativeRetrievalService {

    private final ChatModel llm;
    private final VectorStore vectorStore;
    private static final int MAX_ITERATIONS = 3;

    public String iterativeRetrieve(String query) {
        StringBuilder answer = new StringBuilder();
        Set<String> retrievedDocIds = new HashSet<>();
        int iteration = 0;

        while (iteration < MAX_ITERATIONS) {
            iteration++;

            // Step 1: Retrieve documents
            List<Document> docs = retrieveDocuments(query, retrievedDocIds);

            // Step 2: Generate/refine answer
            String currentAnswer = generateAnswer(query, docs, answer.toString());

            // Step 3: Check for information gaps
            GapAnalysisResult gaps = analyzeGaps(query, currentAnswer);

            if (!gaps.hasGaps()) {
                // Satisfied with current answer
                return currentAnswer;
            }

            // Step 4: Generate follow-up query based on gaps
            query = generateFollowUpQuery(query, currentAnswer, gaps);

            // Track retrieved documents to avoid duplicates
            docs.forEach(d -> retrievedDocIds.add(d.getId()));

            answer = new StringBuilder(currentAnswer);
        }

        return answer.toString();
    }

    private List<Document> retrieveDocuments(String query, Set<String> excludeIds) {
        List<Document> allDocs = vectorStore.similaritySearch(
            SearchRequest.query(query).withTopK(10)
        );

        // Filter out already retrieved documents
        return allDocs.stream()
            .filter(doc -> !excludeIds.contains(doc.getId()))
            .limit(5)
            .toList();
    }

    private String generateAnswer(String query, List<Document> docs, String previousAnswer) {
        String context = docs.stream()
            .map(Document::getContent)
            .collect(Collectors.joining("\n\n"));

        String prompt;
        if (previousAnswer.isEmpty()) {
            // Initial generation
            prompt = """
                Answer this question using the provided context:

                Question: %s

                Context:
                %s

                Provide a comprehensive answer. If the context is insufficient,
                explicitly state what information is missing.
                """.formatted(query, context);
        } else {
            // Refinement
            prompt = """
                Previous Answer:
                %s

                Additional Context:
                %s

                Original Question: %s

                Refine the answer by incorporating the additional context.
                Address any gaps in the previous answer.
                """.formatted(previousAnswer, context, query);
        }

        return llm.call(prompt);
    }

    private GapAnalysisResult analyzeGaps(String query, String answer) {
        String prompt = """
            Analyze this answer for information gaps:

            Question: %s

            Answer: %s

            Identify what information is missing or incomplete.

            Output format:
            HAS_GAPS: true/false
            GAPS: [list of missing information]

            Be conservative - only mark gaps if critical information is truly missing.
            """.formatted(query, answer);

        String response = llm.call(prompt);

        return parseGapAnalysis(response);
    }

    private String generateFollowUpQuery(String originalQuery, String currentAnswer, GapAnalysisResult gaps) {
        String prompt = """
            Original Question: %s

            Current Answer: %s

            Identified Gaps: %s

            Generate a follow-up search query to find the missing information.

            Output only the search query.
            """.formatted(originalQuery, currentAnswer, gaps.description());

        return llm.call(prompt).trim();
    }
}

// Gap Analysis Result
record GapAnalysisResult(boolean hasGaps, List<String> missingInfo) {
    public String description() {
        return String.join(", ", missingInfo);
    }
}

Use Cases:

Multi-part questions ("Compare X and Y, then recommend which is better for Z")
Exploratory research ("Tell me about [topic], starting with basics and going deeper")
Complex troubleshooting ("Debug this error: First check common causes, then rare ones")

Performance:

2-3x improvement in answer completeness
40-60% increase in latency (trade-off vs quality)
Best for low-volume, high-value queries

7.3 GraphRAG - Knowledge Graph Enhanced

7.3.1 Why Graphs Complement Vectors

Vector databases find similarity (semantic closeness):

"DNS" is similar to "network configuration"
Great for: Factual retrieval

Knowledge graphs find relationships (structural connections):

"DNS" → "depends on" → "routing"
Great for: Multi-hop reasoning

Complementary Strengths:

Aspect	Vector DB	Knowledge Graph	Combined
Finds	Similar content	Related entities	Both
Best for	Factual queries	Relational queries	Complex queries
Reasoning	Single-hop	Multi-hop	Adaptive
Example	"How to configure DNS"	"What does DNS depend on?"	Both scenarios

7.3.2 Graph Construction

Technique: Extract (Entity, Relation, Entity) triples from unstructured text.

Process:

Parse documents with LLM
Identify entities (people, concepts, technologies)
Extract relations (verbs, dependencies)
Store as knowledge graph

Entity Extraction in Java:

@Service
public class KnowledgeGraphService {

    private final ChatModel llm;
    private final GraphStore graphStore;

    public void buildGraphFromDocuments(List<Document> documents) {
        for (Document doc : documents) {
            List<Triple> triples = extractTriples(doc);
            storeTriples(triples);
        }
    }

    private List<Triple> extractTriples(Document doc) {
        String prompt = """
            Extract entity-relationship-entity triples from this text:

            Text: %s

            Extract triples in the format:
            SUBJECT | PREDICATE | OBJECT

            Examples:
            - DNS | uses | TCP port 53
            - AdGuard | configures | DNS
            - Router | forwards | DNS queries

            Only extract clear, factual relationships.
            """.formatted(doc.getContent());

        String response = llm.call(prompt);

        return parseTriples(response);
    }

    private List<Triple> parseTriples(String response) {
        return response.lines()
            .filter(line -> line.contains("|"))
            .map(line -> {
                String[] parts = line.split("\\|");
                return new Triple(
                    parts[0].trim(),
                    parts[1].trim(),
                    parts[2].trim()
                );
            })
            .toList();
    }

    private void storeTriples(List<Triple> triples) {
        for (Triple triple : triples) {
            // Create or get nodes
            Node subject = graphStore.getOrCreateNode(triple.subject());
            Node object = graphStore.getOrCreateNode(triple.object());

            // Create relationship
            Relationship rel = subject.createRelationshipTo(
                object,
                triple.predicate()
            );

            graphStore.save(rel);
        }
    }

    public List<GraphPath> findPaths(String startEntity, String endEntity, int maxDepth) {
        return graphStore.findPaths(
            startEntity,
            endEntity,
            maxDepth,
            limit = 10
        );
    }
}

// Triple Record
record Triple(String subject, String predicate, String object) {}

Triple Extraction Patterns:

Pattern	Example	Subject	Predicate	Object
Technology Dependency	"DNS uses TCP port 53"	DNS	uses	TCP port 53
Configuration	"AdGuard configures DNS"	AdGuard	configures	DNS
Causality	"Misconfigured DNS causes resolution failure"	Misconfigured DNS	causes	resolution failure
Part-Of	"TCP is part of the network stack"	TCP	part-of	network stack
Location	"Config file is in /etc/dns"	Config file	located-in	/etc/dns

Tools for Graph Construction:

Tool	Language	Type	Best For
Neo4j	Cypher, Java	Graph Database	Production systems
NebulaGraph	nGQL, Java	Distributed Graph	Large-scale graphs
Microsoft GraphRAG	Python	Automated Pipeline	Quick setup
NetworkX	Python	Library	Prototyping

7.3.3 Graph+Vector Hybrid Retrieval

Algorithm:

Vector Search → Find anchor entities
Graph Traversal → Get 2-hop neighbor nodes
Merge → Combine text chunks + relation paths
Feed to LLM with combined context

Implementation:

@Service
public class HybridRetrievalService {

    private final VectorStore vectorStore;
    private final GraphStore graphStore;

    public HybridRetrievalResult hybridSearch(String query) {
        // Phase 1: Vector search for anchor documents
        List<Document> anchorDocs = vectorStore.similaritySearch(
            SearchRequest.query(query).withTopK(5)
        );

        // Phase 2: Extract entities from anchor docs
        Set<String> entities = extractEntities(anchorDocs);

        // Phase 3: Graph traversal for related entities
        List<GraphPath> paths = new ArrayList<>();
        for (String entity : entities) {
            List<GraphPath> entityPaths = graphStore.findNeighbors(
                entity,
                maxDepth = 2,
                limit = 5
            );
            paths.addAll(entityPaths);
        }

        // Phase 4: Merge context
        String combinedContext = buildCombinedContext(anchorDocs, paths);

        return new HybridRetrievalResult(combinedContext, anchorDocs, paths);
    }

    private Set<String> extractEntities(List<Document> docs) {
        Set<String> entities = new HashSet<>();
        for (Document doc : docs) {
            // Extract entities from document metadata or content
            entities.addAll(doc.getMetadata()
                .getOrDefault("entities", List.of())
                .stream()
                .map(Object::toString)
                .toList());
        }
        return entities;
    }

    private String buildCombinedContext(List<Document> docs, List<GraphPath> paths) {
        StringBuilder context = new StringBuilder();

        context.append("=== Relevant Documents ===\n");
        for (Document doc : docs) {
            context.append(doc.getContent()).append("\n\n");
        }

        context.append("\n=== Related Entity Paths ===\n");
        for (GraphPath path : paths) {
            context.append(path.format())
                    .append("\n");
        }

        return context.toString();
    }
}

// Hybrid Retrieval Result
record HybridRetrievalResult(
    String combinedContext,
    List<Document> documents,
    List<GraphPath> graphPaths
) {}

Performance:

2-3x improvement on multi-hop queries
Comparable performance on simple queries
Best for: "How does X affect Y?" type questions

7.3.4 Community Summary (Microsoft GraphRAG)

Concept: Pre-generate summaries for node communities to handle macro-questions.

Process:

Detect communities (clusters of related entities)
Summarize each community
Index community summaries
For macro-questions ("What trends in this doc?"), retrieve summaries

Local vs Global Retrieval:

Aspect	Local Retrieval	Global Retrieval
Scope	Specific entities	Community summaries
Query Type	"What does X do?"	"What are the trends?"
Context Size	Small (specific)	Large (summarized)
Token Efficiency	Baseline	97% reduction

Implementation:

@Service
public class CommunitySummaryService {

    private final GraphStore graphStore;
    private final ChatModel llm;

    public void buildCommunitySummaries() {
        // Step 1: Detect communities
        List<Community> communities = graphStore.detectCommunities(
            algorithm = "Louvain"
        );

        // Step 2: Summarize each community
        for (Community community : communities) {
            String summary = summarizeCommunity(community);
            community.setSummary(summary);
            graphStore.save(community);
        }
    }

    private String summarizeCommunity(Community community) {
        String entitiesText = community.getEntities().stream()
            .map(Node::getLabel)
            .collect(Collectors.joining(", ""));

        String relationsText = community.getRelations().stream()
            .map(Relationship::toString)
            .collect(Collectors.joining("\n"));

        String prompt = """
            Summarize this community of entities and their relationships:

            Entities: %s

            Relationships:
            %s

            Provide a concise summary (2-3 sentences) describing:
            1. What this community is about
            2. Key patterns or themes
            3. How entities relate to each other
            """.formatted(entitiesText, relationsText);

        return llm.call(prompt);
    }

    public List<String> retrieveCommunitySummaries(String query) {
        // Embed query
        float[] queryEmbedding = embed(query);

        // Find similar community summaries
        List<Community> similarCommunities = graphStore.searchSummaries(
            queryEmbedding,
            topK = 3
        );

        return similarCommunities.stream()
            .map(Community::getSummary)
            .toList();
    }
}

Use Case: Macro-questions that require understanding themes, not specific facts.

Contextual Retrieval (Anthropic, 2024)

Anthropic 提出的 Contextual Retrieval 方法显著提升了检索质量：

核心思想: 在每个 chunk 前添加文档级上下文，使 embedding 和 BM25 索引更准确。

# Contextual Chunk Generation
def create_contextual_chunk(document: str, chunk: str) -> str:
    prompt = f"""<document>
{document}
</document>

Here is the chunk we want to situate within the whole document:
<chunk>
{chunk}
</chunk>

Please give a short succinct context to situate this chunk within the overall document for the purposes of improving search retrieval of the chunk. Answer only with the succinct context and nothing else."""
    
    context = llm.generate(prompt)
    return f"{context}\n\n{chunk}"  # Prepend context to chunk

效果: 在多个基准测试中，Contextual Retrieval 将检索失败率降低了 49%（combined with BM25）到 67%（combined with embeddings）。

LazyGraphRAG (Microsoft, 2024)

LazyGraphRAG 是 GraphRAG 的轻量级替代方案，大幅降低成本：

成本降低 700x+: 不需要昂贵的 LLM 抽取，使用 NLP 技术进行实体和关系抽取
查询时图构建: 在查询时才构建相关子图，而非预先构建全图
适合场景: 中小规模文档集、预算受限的项目

LightRAG

LightRAG 是另一个轻量级图检索方案：

双层检索: 同时支持具体事实查询和抽象概念查询
增量更新: 支持新文档增量插入，无需重建索引
更低资源需求: 相比 GraphRAG 显著降低计算和存储需求

Multimodal RAG

检索和生成不仅限于文本，还支持图片、表格、文档版面：

Method	Description	Best For
Text-based	OCR + text RAG	Scanned documents
ColPali	Visual token matching	Document screenshots, PDFs
Multimodal Embedding	Image+text joint embeddings	Mixed media collections
Native Multimodal	Send images directly to VLM	Complex visual content

# ColPali: Vision-based document retrieval
from colpali_engine.models import ColPali, ColPaliProcessor

model = ColPali.from_pretrained("vidore/colpali-v1.2")
processor = ColPaliProcessor.from_pretrained("vidore/colpali-v1.2")

# Index document pages as images
images = [load_image(page) for page in document_pages]
embeddings = model.forward(processor.process_images(images))

# Query
query_embedding = model.forward(processor.process_queries(["What is the revenue?"]))
scores = embeddings @ query_embedding.T

Agentic RAG (补充)

将 Agent 的自主决策能力融入 RAG 流程：

核心能力:

自适应检索: Agent 根据查询类型自动选择检索策略
迭代检索: 如果首次检索结果不够，Agent 可以改写查询重新检索
多源融合: 同时使用向量搜索、关键词搜索、知识图谱、SQL查询
自我评估: Agent 判断检索结果是否足以回答问题

2026 年 4月前沿：从检索到导航

CorpusGraph（arXiv:2604.14572）提出了一种全新的 Agentic RAG 范式：用 Agent 主动导航企业知识语料库，而非被动检索。传统 RAG 系统无法回溯或组合分散的证据片段，CorpusGraph 让 Agent 学习组织结构，像人类研究员一样在文档间跳转、回溯、整合信息。这一思路正在推动 Agentic RAG 从"检索增强"向"知识导航"演进。

UniDoc-RL（arXiv:2604.14967）则将 RL 引入视觉 RAG，通过分层动作和密集奖励解决细粒度视觉语义的复杂推理问题，将外部视觉知识融入大视觉语言模型。

7.4 Agentic RAG - Autonomous Reasoning

7.4.1 From Reader to Researcher

Agentic RAG: LLM with tool use and self-reflection capabilities.

Shift: Passive responder → Active researcher

Key Capabilities:

Tool Use: Can call external tools (retrieval, calculator, code executor)
Self-Reflection: Evaluates own outputs for quality
Iteration: Improves answers through multiple rounds
Memory: Maintains conversation context

7.4.2 Self-RAG (Self-Reflective RAG)

Research: Self-RAG (Asai et al., 2023, ICLR 2024)

Core Mechanism: Self-scoring during generation with reflection tokens.

Reflection Tokens:

Need Retrieval? - Should I search for information?
Is Relevant? - Is retrieved content useful?
Is Supported? - Is answer grounded in evidence?

Reflection Token Thresholds:

Reflection Token	Question	Threshold	Action
`Need Retrieval?`	Do I need external information?	> 0.7	Trigger retrieval
`Is Relevant?`	Is retrieved context useful?	> 0.6	Use context
`Is Supported?`	Is claim grounded in evidence?	> 0.8	Include claim

Implementation:

@Service
public class SelfRAGService {

    private final ChatModel llm;
    private final VectorStore vectorStore;

    public String selfRAG(String query) {
        // Reflection 1: Need Retrieval?
        boolean needRetrieval = checkNeedRetrieval(query);

        String context = "";
        if (needRetrieval) {
            List<Document> docs = retrieveWithReflection(query);
            context = formatContext(docs);
        }

        // Generate with reflection loop
        String answer;
        int attempts = 0;
        do {
            answer = generateAnswer(query, context);

            // Reflection 3: Is Supported?
            if (checkIsSupported(query, answer, context)) {
                return answer;
            }

            // If not supported, regenerate with adjusted prompt
            attempts++;
        } while (attempts < 3);

        return answer;
    }

    private boolean checkNeedRetrieval(String query) {
        String prompt = """
            Query: %s

            Do you need to retrieve external information to answer this query accurately?

            Consider:
            - Is this about specific documents or facts not in your training data?
            - Is this about recent events or current information?
            - Is this a common sense question you can answer directly?

            Output: NEED_RETRIEVAL or NO_RETRIEVAL
            """.formatted(query);

        String response = llm.call(prompt);
        return response.contains("NEED_RETRIEVAL");
    }

    private List<Document> retrieveWithReflection(String query) {
        List<Document> candidates = vectorStore.similaritySearch(
            SearchRequest.query(query).withTopK(10)
        );

        // Reflection 2: Is Relevant?
        List<Document> relevantDocs = new ArrayList<>();
        for (Document doc : candidates) {
            if (checkIsRelevant(query, doc)) {
                relevantDocs.add(doc);
            }
        }

        return relevantDocs;
    }

    private boolean checkIsRelevant(String query, Document doc) {
        String prompt = """
            Query: %s

            Document: %s

            Is this document relevant to answering the query?

            Output: RELEVANT or NOT_RELEVANT
            """.formatted(query, doc.getContent());

        String response = llm.call(prompt);
        return response.contains("RELEVANT");
    }

    private boolean checkIsSupported(String query, String answer, String context) {
        String prompt = """
            Query: %s

            Answer: %s

            Context: %s

            Is the answer supported by the context?

            Check:
            - Are all claims in the answer present in the context?
            - Does the answer contradict the context?
            - Does the answer rely on external knowledge not in context?

            Output: SUPPORTED or NOT_SUPPORTED
            """.formatted(query, answer, context);

        String response = llm.call(prompt);
        return response.contains("SUPPORTED");
    }

    private String generateAnswer(String query, String context) {
        String prompt = context.isEmpty()
            ? "Answer: %s".formatted(query)
            : """
                Context: %s

                Question: %s

                Answer the question using only the provided context.
                If the context is insufficient, state that clearly.
                """.formatted(context, query);

        return llm.call(prompt);
    }
}

Performance: 15-20% accuracy improvement on complex QA tasks.

7.4.3 Corrective RAG (CRAG)

Research: Corrective RAG (Yan et al., 2024)

Core Mechanism: Lightweight retrieval evaluator + fallback mechanisms.

Algorithm:

Retrieve documents
Evaluate retrieval quality (confidence score)
If Poor → Trigger Web Search or fallback
If Good → Proceed with generation
Generate and verify

Implementation:

@Service
public class CorrectiveRAGService {

    private final ChatModel llm;
    private final VectorStore vectorStore;
    private final WebSearchService webSearch;

    public String correctiveRAG(String query) {
        // Step 1: Initial retrieval
        List<Document> docs = vectorStore.similaritySearch(
            SearchRequest.query(query).withTopK(5)
        );

        // Step 2: Evaluate retrieval quality
        RetrievalQuality quality = evaluateRetrieval(query, docs);

        // Step 3: Route based on quality
        if (quality.isPoor()) {
            // Trigger web search fallback
            List<WebResult> webResults = webSearch.search(query);
            docs = convertWebResultsToDocs(webResults);
        }

        // Step 4: Generate answer
        String answer = generateAnswer(query, docs);

        // Step 5: Verify answer
        if (verifyAnswer(answer, docs)) {
            return answer;
        } else {
            // Fallback to web search if verification fails
            List<WebResult> webResults = webSearch.search(query);
            return generateAnswer(query, convertWebResultsToDocs(webResults));
        }
    }

    private RetrievalQuality evaluateRetrieval(String query, List<Document> docs) {
        String prompt = """
            Query: %s

            Retrieved Documents:
            %s

            Evaluate the quality of these documents for answering the query.

            Criteria:
            1. Relevance: Do the documents address the query?
            2. Completeness: Is sufficient information present?
            3. Accuracy: Is the information consistent and reliable?

            Score: 0-100
            """.formatted(query, formatDocs(docs));

        String response = llm.call(prompt);

        // Extract score from response
        int score = extractScore(response);

        return new RetrievalQuality(score);
    }

    private boolean verifyAnswer(String answer, List<Document> docs) {
        String prompt = """
            Answer: %s

            Source Documents:
            %s

            Verify this answer:
            1. Is the answer grounded in the documents?
            2. Are there any hallucinations?
            3. Is the answer complete?

            Output: VERIFIED or NOT_VERIFIED
            """.formatted(answer, formatDocs(docs));

        String response = llm.call(prompt);
        return response.contains("VERIFIED");
    }
}

record RetrievalQuality(int score) {
    public boolean isPoor() {
        return score < 50;
    }

    public boolean isGood() {
        return score >= 70;
    }
}

Performance: 15-20% accuracy improvement, especially on queries with poor initial retrieval.

7.4.4 Tool Use

Scenario: RAG retrieves "2023 revenue data", user asks "YoY growth rate"

Problem: LLM can't calculate from raw numbers alone

Solution: Agent retrieves data → Calls Python interpreter → Returns computed result

@Service
public class ToolUseAgent {

    private final ChatModel llm;
    private final VectorStore vectorStore;
    private final CalculatorTool calculator;
    private final CodeExecutorTool codeExecutor;

    public String executeWithTools(String query) {
        // Step 1: Decide which tools to use
        ToolPlan plan = decideTools(query);

        // Step 2: Execute tools in sequence
        StringBuilder context = new StringBuilder();

        for (ToolAction action : plan.actions()) {
            String result = executeTool(action);
            context.append(result).append("\n\n");
        }

        // Step 3: Generate final answer with tool results
        return generateAnswer(query, context.toString());
    }

    private ToolPlan decideTools(String query) {
        String prompt = """
            Query: %s

            Available tools:
            1. RETRIEVE - Search document database
            2. CALCULATOR - Perform calculations
            3. CODE_EXECUTOR - Execute Python code

            Plan which tools to use and in what order.

            Output format:
            TOOL: tool_name
            INPUT: input_for_tool

            Repeat for each tool needed.
            """.formatted(query);

        String response = llm.call(prompt);

        return parseToolPlan(response);
    }

    private String executeTool(ToolAction action) {
        return switch (action.tool()) {
            case "RETRIEVE" -> {
                List<Document> docs = vectorStore.similaritySearch(
                    SearchRequest.query(action.input()).withTopK(5)
                );
                yield formatDocs(docs);
            }
            case "CALCULATOR" -> calculator.calculate(action.input());
            case "CODE_EXECUTOR" -> codeExecutor.execute(action.input());
            default -> "Unknown tool";
        };
    }
}

// Tool Records
record ToolPlan(List<ToolAction> actions) {}
record ToolAction(String tool, String input) {}

@Component
class CalculatorTool {
    public String calculate(String expression) {
        // Evaluate mathematical expression safely
        ScriptEngineManager mgr = new ScriptEngineManager();
        ScriptEngine engine = mgr.getEngineByName("JavaScript");
        try {
            Object result = engine.eval(expression);
            return "Calculation result: " + result;
        } catch (ScriptException e) {
            return "Error: " + e.getMessage();
        }
    }
}

@Component
class CodeExecutorTool {
    public String execute(String code) {
        // Execute Python code in sandboxed environment
        // This is a simplified example - production requires proper sandboxing
        ProcessBuilder pb = new ProcessBuilder("python3", "-c", code);
        try {
            Process process = pb.start();
            BufferedReader reader = new BufferedReader(
                new InputStreamReader(process.getInputStream())
            );
            StringBuilder output = new StringBuilder();
            String line;
            while ((line = reader.readLine()) != null) {
                output.append(line).append("\n");
            }
            return output.toString();
        } catch (IOException e) {
            return "Error executing code: " + e.getMessage();
        }
    }
}

Common Tools:

Tool	Use Case	Example
RETRIEVE	Search documents	"Find revenue data for 2023"
CALCULATOR	Perform calculations	"Calculate YoY growth"
CODE_EXECUTOR	Run Python/JavaScript	"Plot this data"
WEB_SEARCH	Get recent information	"Latest stock price"
DATABASE_QUERY	Query SQL database	"Get user count"

7.5 RAG + Fine-tuning Fusion

7.5.1 Complementary, Not Competing

Concept: RAG + Fine-tuning > Either alone

Complementarity:

Aspect	RAG	Fine-tuning	Combined
Knowledge	External (documents)	Internal (weights)	Both
Updates	Real-time (add docs)	Slow (retrain)	Flexible
Cost	Per-query (API calls)	One-time (training)	Balanced
Domain Adaptation	Weak	Strong	Optimal
Hallucination	Possible (wrong retrieval)	Possible (wrong training)	Reduced

7.5.2 Embedding Fine-tuning (Domain Adaptation)

Problem: General models (OpenAI) don't understand industry jargon.

Domains: Medical (terminology), Legal (case law), Finance (regulations).

Solution: Train domain-specific embedding models.

Data Construction:

# Pseudocode: Contrastive learning for embedding fine-tuning
def construct_training_data(documents):
    """
    Create positive and negative pairs for contrastive learning

    Positive pairs: Similar domain-specific documents
    Negative pairs: Dissimilar documents
    """
    training_pairs = []

    for doc in documents:
        # Positive: Same topic, similar content
        positive = find_similar_document(doc, documents)

        # Negative: Different topic or dissimilar content
        negative = find_dissimilar_document(doc, documents)

        training_pairs.append({
            "anchor": doc,
            "positive": positive,
            "negative": negative
        })

    return training_pairs


def contrastive_loss(anchor_emb, positive_emb, negative_emb, temperature=0.07):
    """
    InfoNCE loss for contrastive learning

    Pull similar items together, push dissimilar apart
    """
    # Similarity scores
    pos_sim = cosine_similarity(anchor_emb, positive_emb) / temperature
    neg_sim = cosine_similarity(anchor_emb, negative_emb) / temperature

    # Contrastive loss
    loss = -log(exp(pos_sim) / (exp(pos_sim) + exp(neg_sim)))

    return loss

Training Loop:

# Pseudocode: Fine-tuning loop
def fine_tune_embedding_model(model, training_data, epochs=10):
    optimizer = Adam(model.parameters(), lr=1e-5)

    for epoch in range(epochs):
        total_loss = 0

        for batch in training_data:
            # Forward pass
            anchor_emb = model.encode(batch["anchor"])
            positive_emb = model.encode(batch["positive"])
            negative_emb = model.encode(batch["negative"])

            # Compute loss
            loss = contrastive_loss(anchor_emb, positive_emb, negative_emb)

            # Backward pass
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            total_loss += loss.item()

        avg_loss = total_loss / len(training_data)
        print(f"Epoch {epoch+1}/{epochs}, Loss: {avg_loss:.4f}")

    return model

Tools:

BGE-M3: Multilingual embedding fine-tuning
E5: English embedding fine-tuning
Sentence Transformers: HuggingFace library

Results: 10-15% retrieval improvement in specialized domains.

7.5.3 RAFT (Retrieval Augmented Fine Tuning)

Research: RAFT (Zhang et al., 2024)

Core Idea: Train LLM to "read RAG context correctly"

Data Format:

Question
Distractor Docs (noise documents to ignore)
Relevant Docs (gold documents to use)
Chain-of-Thought reasoning
Answer (with citations)

Data Construction:

@Service
public class RaftDataBuilder {

    private final ChatModel llm;
    private final VectorStore vectorStore;

    public RaftTrainingExample buildRaftExample(String question, Document goldDocument) {
        // Step 1: Find relevant documents (gold)
        List<Document> relevantDocs = List.of(goldDocument);

        // Step 2: Find distractor documents (similar but not relevant)
        List<Document> distractorDocs = findDistractors(question, goldDocument, k=4);

        // Step 3: Generate chain-of-thought reasoning
        String cot = generateCoT(question, relevantDocs, distractorDocs);

        // Step 4: Generate answer with citations
        String answer = generateAnswerWithCitations(question, relevantDocs, cot);

        return new RaftTrainingExample(
            question,
            distractorDocs,
            relevantDocs,
            cot,
            answer
        );
    }

    private List<Document> findDistractors(String question, Document goldDoc, int k) {
        // Retrieve similar documents
        List<Document> candidates = vectorStore.similaritySearch(
            SearchRequest.query(question).withTopK(20)
        );

        // Filter out the gold document
        return candidates.stream()
            .filter(doc -> !doc.getId().equals(goldDoc.getId()))
            .filter(doc -> !isRelevant(doc, question))  // Must be irrelevant
            .limit(k)
            .toList();
    }

    private boolean isRelevant(Document doc, String question) {
        // Use LLM to check relevance
        String prompt = """
            Question: %s

            Document: %s

            Is this document relevant to answering the question?

            Output: RELEVANT or NOT_RELEVANT
            """.formatted(question, doc.getContent());

        String response = llm.call(prompt);
        return response.contains("RELEVANT");
    }

    private String generateCoT(String question, List<Document> relevant, List<Document> distractors) {
        String context = buildContext(relevant, distractors);

        String prompt = """
            Question: %s

            Context:
            %s

            Generate step-by-step reasoning to answer the question.

            Instructions:
            1. Identify which documents are relevant (ignore distractors)
            2. Extract key information from relevant documents
            3. Reason through to the answer
            4. Cite sources using [Doc N]

            Format your reasoning clearly with numbered steps.
            """.formatted(question, context);

        return llm.call(prompt);
    }

    private String generateAnswerWithCitations(String question, List<Document> relevant, String cot) {
        String prompt = """
            Question: %s

            Reasoning:
            %s

            Based on the reasoning above, provide a concise answer.

            Include citations using [Doc N] format where:
            Doc 0: %s
            Doc 1: %s
            ...etc
            """.formatted(
                question,
                cot,
                relevant.get(0).getId(),
                relevant.size() > 1 ? relevant.get(1).getId() : "N/A"
            );

        return llm.call(prompt);
    }

    private String buildContext(List<Document> relevant, List<Document> distractors) {
        StringBuilder context = new StringBuilder();

        context.append("=== Relevant Documents ===\n");
        for (int i = 0; i < relevant.size(); i++) {
            context.append(String.format("Doc %d: %s\n\n", i, relevant.get(i).getContent()));
        }

        context.append("\n=== Additional Documents ===\n");
        for (int i = 0; i < distractors.size(); i++) {
            context.append(String.format("Doc %d: %s\n\n",
                i + relevant.size(),
                distractors.get(i).getContent()
            ));
        }

        return context.toString();
    }
}

// RAFT Training Example
record RaftTrainingExample(
    String question,
    List<Document> distractorDocs,
    List<Document> relevantDocs,
    String chainOfThought,
    String answer
) {}

Training Objective:

Learn to ignore distractor documents
Focus on relevant documents
Cite evidence properly
Synthesize from multiple sources

Results: 20-30% improvement on domain QA tasks.

7.6 Performance Optimization

7.6.1 Context Caching

Problem: Repeated embedding computation for same prompts/docs.

Solution: Cache in KV Cache (DeepSeek, Anthropic Claude support).

Technique: Cache long system prompts, common document sets.

@Service
public class CachedRAGService {

    private final ChatModel llm;
    private final VectorStore vectorStore;
    private final Cache<String, String> promptCache;

    public String cachedQuery(String query) {
        // Step 1: Check cache
        String cacheKey = generateCacheKey(query);

        String cachedResponse = promptCache.getIfPresent(cacheKey);
        if (cachedResponse != null) {
            return cachedResponse + " (cached)";
        }

        // Step 2: Build cache-aware prompt
        String systemPrompt = loadSystemPrompt();  // Cached by LLM provider
        String context = retrieveContext(query);

        // Step 3: Generate with caching enabled
        String response = llm.call(ChatPrompt.builder()
            .system(systemPrompt)
            .user(buildUserPrompt(query, context))
            .build()
        );

        // Step 4: Cache the response
        promptCache.put(cacheKey, response);

        return response;
    }

    private String retrieveContext(String query) {
        List<Document> docs = vectorStore.similaritySearch(
            SearchRequest.query(query).withTopK(5)
        );

        return docs.stream()
            .map(Document::getContent)
            .collect(Collectors.joining("\n\n"));
    }

    private String buildUserPrompt(String query, String context) {
        return """
            Context:
            %s

            Question: %s

            Answer the question using the context above.
            """.formatted(context, query);
    }

    private String generateCacheKey(String query) {
        // Simple hash-based cache key
        return DigestUtils.sha256Hex(query);
    }
}

Caching Strategies:

Strategy	What to Cache	Benefit	Use Case
Prompt Cache	System prompts, instructions	90% latency reduction for cached content	Repeated prompts
Document Cache	Frequently retrieved docs	Skip vector search	FAQ-type queries
Semantic Cache	Similar queries (embeddings)	Answer similar questions	High volume

Results: 90% latency reduction for cached content.

7.6.2 Speculative RAG

Problem: Large models are slow, small models are inaccurate.

Solution: Small model draft → Large model verify + retrieve.

Implementation:

@Service
public class SpeculativeRAGService {

    private final ChatModel smallModel;  // Fast (e.g., GPT-4o-mini)
    private final ChatModel largeModel;  // Accurate (e.g., GPT-4o)
    private final VectorStore vectorStore;

    public String speculativeRAG(String query) {
        // Phase 1: Small model draft
        String draftAnswer = smallModel.call(
            "Answer this question: %s".formatted(query)
        );

        // Phase 2: Quality check
        double quality = checkQuality(query, draftAnswer);

        if (quality > 0.8) {
            // Draft is good enough, return it
            return draftAnswer;
        }

        // Phase 3: Large model refinement with retrieval
        List<Document> docs = vectorStore.similaritySearch(
            SearchRequest.query(query).withTopK(5)
        );

        String refinedPrompt = """
            Draft Answer: %s

            Retrieved Context:
            %s

            Question: %s

            Refine the draft answer using the retrieved context.
            Improve accuracy and completeness.
            """.formatted(
                draftAnswer,
                formatDocs(docs),
                query
            );

        return largeModel.call(refinedPrompt);
    }

    private double checkQuality(String query, String answer) {
        // Use LLM to score quality
        String prompt = """
            Question: %s

            Answer: %s

            Score the quality of this answer (0.0 to 1.0):
            - 1.0: Accurate, complete, well-structured
            - 0.5: Partially correct, missing information
            - 0.0: Incorrect or irrelevant

            Output only the score.
            """.formatted(query, answer);

        String response = smallModel.call(prompt);

        try {
            return Double.parseDouble(response.trim());
        } catch (NumberFormatException e) {
            return 0.5;  // Default to medium quality
        }
    }

    private String formatDocs(List<Document> docs) {
        return docs.stream()
            .map(Document::getContent)
            .collect(Collectors.joining("\n\n"));
    }
}

Trade-off Analysis:

Approach	Speed	Accuracy	Cost	Best For
Small Model Only	Fast	Lower	Low	Simple queries
Large Model Only	Slow	Higher	High	Complex queries
Speculative RAG	Medium	High	Medium	Mixed workloads

7.6.3 Binary Quantization

Problem: Float32 vectors consume huge memory.

Solution: Compress to Int1 (0/1 binary representation).

Algorithm: Scalar quantization → Binarization

@Service
public class QuantizationService {

    public BinaryVector quantize(float[] vector) {
        // Step 1: Normalize vector
        float[] normalized = normalize(vector);

        // Step 2: Binarize (0/1 based on sign)
        byte[] binary = new byte[normalized.length];
        for (int i = 0; i < normalized.length; i++) {
            binary[i] = (byte) (normalized[i] >= 0 ? 1 : 0);
        }

        return new BinaryVector(binary);
    }

    private float[] normalize(float[] vector) {
        // L2 normalization
        float norm = 0;
        for (float v : vector) {
            norm += v * v;
        }
        norm = (float) Math.sqrt(norm);

        float[] normalized = new float[vector.length];
        for (int i = 0; i < vector.length; i++) {
            normalized[i] = vector[i] / norm;
        }

        return normalized;
    }

    public int hammingDistance(BinaryVector a, BinaryVector b) {
        // Fast binary distance calculation
        int distance = 0;
        byte[] va = a.data();
        byte[] vb = b.data();

        for (int i = 0; i < va.length; i++) {
            if (va[i] != vb[i]) {
                distance++;
            }
        }

        return distance;
    }
}

record BinaryVector(byte[] data) {}

Quantization Levels Comparison:

Precision	Memory	Accuracy	Speed	Use Case
FP32 (baseline)	100%	100%	1x	Benchmark
FP16	50%	99%	2x	Production default
INT8	25%	97%	4x	Cost optimization
INT4	12.5%	95%	8x	Edge deployment
INT1 (binary)	3%	92%	10x	Large-scale systems

Results:

30x memory reduction (FP32 → INT1)
10x speed improvement
2-3% accuracy loss (acceptable trade-off)

Use Case: Edge deployment, large-scale systems

Summary

Key Takeaways

Modular RAG:

✅ Dynamic routing adapts to query complexity
✅ Iterative retrieval handles multi-part questions
✅ 30-40% reduction in unnecessary operations
✅ Production frameworks: UltraRAG, CyberRAG, LangGraph

GraphRAG:

✅ Solves multi-hop reasoning through relationship traversal
✅ Community summaries for macro understanding
✅ 77.6% MRR improvement on complex queries
✅ Tools: Microsoft GraphRAG, Neo4j, NebulaGraph

Agentic RAG:

✅ Self-reflection (Self-RAG) reduces hallucinations
✅ Corrective feedback (CRAG) improves accuracy
✅ Tool use enables computation on retrieved data
✅ 15-20% accuracy improvement

RAG + Fine-tuning:

✅ Embedding fine-tuning for domain adaptation
✅ RAFT teaches models to use retrieved context
✅ 20-30% improvement in specialized domains
✅ Critical for medical, legal, financial applications

Performance Optimization:

✅ Context caching: 90% latency reduction
✅ Speculative RAG: Balanced speed and accuracy
✅ Binary quantization: 30x memory savings
✅ Essential for production systems

Production Decision Guide

Scenario	Recommended Technique	Priority	Expected Improvement
High traffic, simple queries	Optimization + Modular RAG	High	30-40% cost reduction
Complex relationships	GraphRAG	Medium	2-3x MRR improvement
Accuracy-critical (medical/legal)	Agentic RAG + Fine-tuning	High	15-30% accuracy gain
Specialized domain	RAG + Embedding fine-tuning	Medium	10-15% retrieval gain
Budget-constrained	Quantization + Caching	High	90% latency reduction
Enterprise-grade	Modular + Graph + Agentic	Medium	Combined benefits

7.1 RAG Challenges & Decision Matrix​

7.1.1 The Production Gap​

7.1.2 Decision Matrix​

7.2 Modular RAG - From Linear to Adaptive​

7.2.1 Paradigm Shift: Linear vs Modular​

7.2.2 Dynamic Routing (Semantic Router)​

7.2.3 Iterative Retrieval (ITER-RETGEN)​

7.3 GraphRAG - Knowledge Graph Enhanced​

7.3.1 Why Graphs Complement Vectors​

7.3.2 Graph Construction​

7.3.3 Graph+Vector Hybrid Retrieval​

7.3.4 Community Summary (Microsoft GraphRAG)​

Contextual Retrieval (Anthropic, 2024)​

LazyGraphRAG (Microsoft, 2024)​

LightRAG​

Multimodal RAG​

Agentic RAG (补充)​

7.4 Agentic RAG - Autonomous Reasoning​

7.4.1 From Reader to Researcher​

7.4.2 Self-RAG (Self-Reflective RAG)​

7.4.3 Corrective RAG (CRAG)​

7.4.4 Tool Use​

7.5 RAG + Fine-tuning Fusion​

7.5.1 Complementary, Not Competing​

7.5.2 Embedding Fine-tuning (Domain Adaptation)​

7.5.3 RAFT (Retrieval Augmented Fine Tuning)​

7.6 Performance Optimization​

7.6.1 Context Caching​

7.6.2 Speculative RAG​

7.6.3 Binary Quantization​

Summary​

Key Takeaways​

Production Decision Guide​

Further Reading​

7.1 RAG Challenges & Decision Matrix

7.1.1 The Production Gap

7.1.2 Decision Matrix

7.2 Modular RAG - From Linear to Adaptive

7.2.1 Paradigm Shift: Linear vs Modular

7.2.2 Dynamic Routing (Semantic Router)

7.2.3 Iterative Retrieval (ITER-RETGEN)

7.3 GraphRAG - Knowledge Graph Enhanced

7.3.1 Why Graphs Complement Vectors

7.3.2 Graph Construction

7.3.3 Graph+Vector Hybrid Retrieval

7.3.4 Community Summary (Microsoft GraphRAG)

Contextual Retrieval (Anthropic, 2024)

LazyGraphRAG (Microsoft, 2024)

LightRAG

Multimodal RAG

Agentic RAG (补充)

7.4 Agentic RAG - Autonomous Reasoning

7.4.1 From Reader to Researcher

7.4.2 Self-RAG (Self-Reflective RAG)

7.4.3 Corrective RAG (CRAG)

7.4.4 Tool Use

7.5 RAG + Fine-tuning Fusion

7.5.1 Complementary, Not Competing

7.5.2 Embedding Fine-tuning (Domain Adaptation)

7.5.3 RAFT (Retrieval Augmented Fine Tuning)

7.6 Performance Optimization

7.6.1 Context Caching

7.6.2 Speculative RAG

7.6.3 Binary Quantization

Summary

Key Takeaways

Production Decision Guide

Further Reading