RAG Retrieval & Accuracy
Overview
Retrieval accuracy is where RAG systems succeed or fail. You can have perfect data integration, optimal chunking, and high-quality embeddings, but if the wrong documents are retrieved—or the right documents aren't found—your agents will provide poor answers. This section focuses on ensuring your RAG pipeline retrieves the most relevant, accurate, and complete information for every query.
Why Retrieval Accuracy Matters
Accurate retrieval ensures:
Correct answers - LLM receives the right context to answer questions
Complete information - All relevant facts are provided, not just fragments
Trustworthy responses - Answers grounded in real, cited sources
User confidence - Consistent, reliable performance builds trust
Poor retrieval leads to:
Wrong answers - Irrelevant context leads to incorrect responses
"I don't know" responses - Relevant information exists but isn't found
Hallucinations - LLM fills knowledge gaps with fabricated information
Inconsistent quality - Some queries work perfectly, others fail completely
User frustration - Unreliable agent → low adoption → wasted investment
Common Retrieval Challenges
Retrieval Failures
No relevant chunks retrieved - Known information not found
Wrong documents ranked highly - Irrelevant content returned first
Incomplete context assembly - Partial information missing key details
Multi-hop reasoning failure - Cannot connect related pieces of information
Source Quality
Outdated knowledge base - Stale information leads to wrong answers
Conflicting sources - Contradictory information in retrieved context
Source ranking issues - Lower-quality sources prioritized over authoritative ones
Knowledge base drift - Performance degrades as content changes
Query Understanding
Query-document mismatch - Query phrasing doesn't match how docs are written
Ambiguous query expansion - Query rewriting makes things worse
Context relevance decay - Initially good results become less relevant
Solutions in This Section
Browse these guides to improve retrieval accuracy:
The Retrieval Pipeline
Understanding the stages helps diagnose where failures occur:
Common failure points:
Query Enhancement: Misinterprets intent, adds noise
Vector Search: Embedding doesn't capture query meaning
Candidate Retrieval: Relevant docs not in top candidates
Filtering: Over-aggressive filtering removes good results
Reranking: Wrong docs prioritized
Context Assembly: Important information left out or poorly ordered
Retrieval Strategies
Different approaches for different scenarios:
1. Dense Retrieval (Vector Search)
How it works: Embed query and documents, find nearest neighbors
Strengths:
Semantic understanding
Handles synonyms and paraphrasing
Language-agnostic
Weaknesses:
Poor with rare terms or exact matches
Opaque (hard to debug)
Sensitive to embedding quality
Best for: Natural language queries, conceptual questions
2. Sparse Retrieval (Keyword Search)
How it works: TF-IDF, BM25, or keyword matching
Strengths:
Excellent for exact term matches
Fast and explainable
Works well with technical terms
Weaknesses:
No semantic understanding
Misses synonyms and variations
Language-specific
Best for: Technical queries, product names, error codes
3. Hybrid Retrieval
How it works: Combine dense + sparse, merge results
Strengths:
Best of both worlds
More robust across query types
Handles both semantic and exact matching
Weaknesses:
More complex to implement and tune
Need to balance weighting
Best for: Production RAG systems handling diverse queries
4. Multi-Stage Retrieval
How it works:
Broad retrieval (vector or keyword)
Precise reranking (cross-encoder)
Optional LLM-based final selection
Strengths:
High recall + high precision
Best accuracy for critical applications
Weaknesses:
Higher latency
Increased cost
Best for: High-stakes queries where accuracy is critical
Best Practices
Query Enhancement
Query expansion - Add synonyms, related terms (but test carefully)
Query rewriting - Rephrase for better matching ("How do I..." → "Steps to...")
Spell correction - Fix typos before search
Intent detection - Route different query types differently
Retrieval Configuration
Tune candidate count - Retrieve enough (20-100) to ensure relevant docs are included
Set appropriate thresholds - Don't retrieve below a minimum similarity score
Use metadata filters - Narrow by date, source, topic when applicable
Implement fallbacks - If vector search fails, try keyword search
Reranking
Use cross-encoder models - More accurate than embedding similarity alone
Consider LLM reranking - Ask LLM to rank relevance (expensive but effective)
Diversity in results - Don't return 10 chunks from the same document
Recency boost - Favor newer content when appropriate
Context Assembly
Order matters - Put most relevant context first and last (primacy/recency)
Deduplicate - Remove redundant chunks
Include metadata - Source, date, confidence helps LLM assess reliability
Stay under token limit - Truncate intelligently if needed
Continuous Improvement
Log all queries and retrievals - Build datasets for analysis
Track retrieval metrics - Precision, recall, MRR, NDCG
Collect user feedback - Thumbs up/down on answers
A/B test changes - Compare retrieval strategies empirically
Monitor edge cases - Focus on query types with high failure rates
Retrieval Metrics
Measure these to track accuracy:
Retrieval Quality
Precision@k - Of top k results, how many are relevant?
Recall@k - Of all relevant docs, what % are in top k?
MRR (Mean Reciprocal Rank) - Average of 1/rank of first relevant doc
NDCG (Normalized Discounted Cumulative Gain) - Quality of ranking
Answer Quality
Groundedness - % of response claims supported by retrieved context
Completeness - Does response fully answer the question?
Citation accuracy - Are citations correct and verifiable?
User satisfaction - Thumbs up/down, ratings
System Health
Zero-result queries - % of queries with no retrieval
Low-confidence retrievals - % below similarity threshold
Retrieval latency - P50, P95, P99 times
Cost per query - Embedding, vector search, reranking costs
Advanced Techniques
Query Decomposition
Break complex questions into sub-queries:
User: "How did revenue change from Q1 to Q2, and what caused it?"
Decomposed:
"What was Q1 revenue?"
"What was Q2 revenue?"
"What factors affected Q2 revenue?"
Retrieve separately, synthesize answer from combined results.
Hypothetical Document Embeddings (HyDE)
Instead of embedding the question:
Use LLM to generate hypothetical answer
Embed the hypothetical answer
Search for documents similar to that answer
Works well when questions don't resemble documents.
Multi-Vector Retrieval
Store multiple embeddings per document:
Summary embedding (for high-level matches)
Detailed embedding (for specific facts)
Question embeddings (for FAQ matching)
Retrieve using appropriate embedding type per query.
Contextual Retrieval
Prepend each chunk with document context before embedding:
Original chunk: "Revenue increased 15%"
With context: "[Q2 2024 Financial Report] Revenue increased 15%"
This preserves context and improves retrieval accuracy.
Iterative Retrieval
Retrieve initial context
Generate preliminary answer
Identify gaps or follow-up questions
Retrieve additional context
Generate final answer
Useful for complex, multi-part questions.
Quick Diagnostics
Signs your retrieval needs improvement:
✗ Agents say "I don't know" when answer is in knowledge base
✗ Retrieved chunks don't seem relevant to the question
✗ Correct answer exists but not in top results
✗ Responses are vague or incomplete
✗ Similar queries get wildly different results
✗ Agents hallucinate despite having relevant data
✗ High similarity scores but poor answer quality
Signs your retrieval is working well:
✓ Retrieved chunks are clearly relevant to query
✓ Correct information consistently appears in top results
✓ Complete answers without hallucination
✓ Appropriate "I don't know" when info truly doesn't exist
✓ Good performance across diverse query types
✓ Citations are accurate and helpful
✓ Users report high satisfaction
Debugging Poor Retrieval
When retrieval fails, investigate systematically:
Step 1: Is the information in the knowledge base?
Search manually for the answer
Check if source document was ingested
Verify document processed and chunked correctly
Step 2: Are relevant chunks being retrieved?
Inspect top 10-20 candidate chunks
Check similarity scores
Look at chunk content vs query
Step 3: Is the query being understood correctly?
Review query embedding
Test query variations
Check query enhancement/rewriting
Step 4: Is reranking working?
Compare pre- and post-reranking results
Check reranker scores
Test different reranking strategies
Step 5: Is context assembled well?
Review final context sent to LLM
Check ordering and completeness
Verify token limits not exceeded
Bottom line: Retrieval accuracy is the linchpin of RAG. If your retrieval is poor, no amount of prompt engineering or LLM sophistication will save you. Invest time in getting this right—it's the highest-leverage improvement you can make.
Last updated