Retrieval Systems includes seven components: chunking strategies for splitting documents, embedding model selection for converting text to vectors, query transformation for rewriting searches, hybrid search for combining keyword and semantic matching, reranking for reordering results by relevance, relevance thresholds for filtering quality, and citation tracking for linking answers to sources. The right retrieval stack depends on your document types, query patterns, and accuracy requirements. Most RAG systems need chunking, hybrid search, and reranking working together.
Your team built an internal knowledge base. Uploaded every SOP, process doc, and decision record.
Someone asks "how do we handle refunds?" and the AI responds confidently with completely wrong information.
The correct answer is in there. You can find it manually in 30 seconds. But the AI retrieved the wrong documents.
Retrieval is the difference between an AI that helps and an AI that hallucinates.
Part of Layer 2: Intelligence Infrastructure - How AI finds information.
Retrieval Systems is about getting the right information to your AI before it generates an answer. The best language model in the world produces garbage if fed the wrong context. These components control what gets found.
Most retrieval failures are not search problems. They are pipeline problems. Bad chunking means good documents never get found. Wrong embedding model means similar concepts look different. Missing reranking means the right answer exists but ranks #15 instead of #1.
These components form a chain: documents get chunked, embedded, searched, filtered, and cited. Each step affects what reaches your AI.
Chunking | Embeddings | Query Transform | Hybrid Search | Reranking | Thresholds | Citations | |
|---|---|---|---|---|---|---|---|
| Pipeline Stage | Ingestion - splitting documents | ||||||
| What It Fixes | Documents too large to retrieve | ||||||
| When to Add | Always - required for any retrieval |
Different symptoms point to different components. Identify what is breaking to know where to focus.
“AI says the answer does not exist but I can find it manually”
The document was split badly. The answer exists but not as a retrievable unit.
“Search for "PTO policy" returns nothing but "vacation guidelines" exists”
Vocabulary mismatch. The query needs expansion or rewriting.
“Technical terms get fuzzy matches instead of exact documents”
Semantic search alone misses exact terms. Add keyword matching.
“The right answer appears in results but at position #12”
Initial search found it. A second pass would rank it higher.
“AI gives confident answers based on loosely related documents”
Low-quality results are reaching the AI. Add a quality filter.
“Users do not trust AI answers because they cannot verify them”
Link every answer to its source documents for verification.
Answer a few questions to identify which component to focus on.
Retrieval is not about AI. It is about finding the right information when you need it. The same patterns apply whether the asker is a person, an AI, or an automated process.
Someone needs information that exists somewhere in your systems
Transform the question, search multiple ways, filter quality, link to sources
The right information reaches the right context for the right decision
When onboarding a new hire means watching them struggle to find answers...
That's a retrieval problem - knowledge exists but is not findable with natural questions.
When answering "what happened last quarter" means searching 5 different places...
That's a retrieval problem - information is scattered and not queryable together.
When nobody can find the right procedure because they use different words...
That's a query transformation problem - vocabulary mismatch between askers and documents.
When you cannot delegate because context is trapped in your head...
That's a retrieval and citation problem - decisions need to link back to their sources.
Which of these sounds most like your current situation?
These mistakes compound. One bad decision in the pipeline pollutes everything downstream.
Move fast. Structure data “good enough.” Scale up. Data becomes messy. Painful migration later. The fix is simple: think about access patterns upfront. It takes an hour now. It saves weeks later.
A retrieval system finds relevant information from a knowledge base to feed into an AI for generating answers. It includes document chunking, embedding generation, search algorithms, and result filtering. The goal is to surface exactly the right context so the AI produces accurate, grounded responses instead of hallucinating. Poor retrieval means wrong answers even with a great AI model.
Keyword search finds exact word matches. If you search "PTO policy," it only finds documents containing those exact words. Semantic search uses embeddings to understand meaning, so "PTO policy" also matches "vacation guidelines" and "time off procedures." Keyword search is precise but inflexible. Semantic search understands intent but can miss exact terms. Hybrid search combines both for better coverage.
Chunking is how you split documents into searchable pieces. Too large (whole documents) and searches return too much irrelevant content. Too small (sentences) and you lose context. The chunk size and boundaries determine what can be retrieved. Split a procedure in the middle and the AI only gets half the steps. Chunking quality directly affects answer quality.
Match the model to your content and queries. General-purpose models work for most cases. Domain-specific models (legal, medical, technical) understand specialized vocabulary better. Consider query type: asymmetric models work better when short queries search long documents. Check operational constraints too: API models are convenient, self-hosted models keep data private. Test with your actual queries.
Reranking takes initial search results and reorders them by actual relevance. Fast retrieval gets you candidates; reranking picks the best ones. A cross-encoder model reads the query and each result together, scoring true relevance. Use reranking when the right answer is in your results but not at the top. It adds latency but dramatically improves which content reaches your AI.
Hybrid search runs both keyword and semantic search, then merges the results. Keyword search catches exact terms like product codes and form numbers. Semantic search catches meaning matches where vocabulary differs. Reciprocal Rank Fusion combines the rankings. Items found by both methods score highest. This covers more edge cases than either method alone.
Relevance thresholds filter results by quality score before they reach your AI. A score of 0.92 means highly relevant. A score of 0.47 means loosely related. Without thresholds, the AI gets every result including garbage. Set a cutoff like 0.75 and only quality content passes through. Tune the threshold based on testing: too high and you miss valid answers, too low and you include noise.
The biggest mistakes: chunking without respecting document structure (cutting procedures in half), using the same embedding model for all content types, skipping hybrid search because semantic feels smarter, reranking before fixing bad retrieval (you cannot reorder what was never found), and ignoring the no-results case (the AI hallucinates when given empty context). Test with real queries throughout.
Query transformation rewrites user questions to better match how documents are written. "How do I request time off" expands to include "PTO," "vacation," "leave request." The system searches with multiple variations and combines results. This bridges vocabulary mismatch between how users ask and how content is written. It dramatically improves recall for knowledge bases with inconsistent terminology.
Citation tracking maintains links from AI answers back to source documents. During retrieval, each chunk keeps metadata about its origin: document name, section, page number. When the AI uses that chunk to answer, the citation travels with it. Users can click through to verify. This transforms "the AI said so" into "the AI found this in Section 4.2 of the Operations Manual."
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