Vector similarity search is just the beginning. Here's how to build RAG systems that actually work for complex enterprise use cases.
Retrieval-Augmented Generation (RAG) has become the go-to pattern for building AI applications that need access to private data. The basic setup is simple: embed your documents, store them in a vector database, retrieve relevant chunks, and feed them to an LLM.
But basic RAG hits walls quickly. Users ask questions that span multiple documents. Context windows fill up with irrelevant chunks. Answers miss crucial information that was "close but not quite" similar enough to retrieve.
Here's how to build RAG systems that actually work.
Before optimizing, understand why retrieval fails:
Semantic mismatch - The user's query uses different terminology than the documents. "How do I get reimbursed?" vs. documents that talk about "expense claims."
Context fragmentation - Relevant information is spread across multiple chunks that don't get retrieved together.
Recency blindness - Vector similarity doesn't understand time. The most relevant answer might be the most recent, not the most similar.
Specificity problems - Generic questions retrieve generic content, missing the specific answer buried in detailed documents.
Single-step retrieval rarely performs well on complex queries. We use multi-stage approaches:
Cast a wide net. Retrieve more documents than you'll ultimately use (top 50-100 instead of top 5-10).
Use a cross-encoder reranker to score each candidate against the query. This is slower but much more accurate than embedding similarity alone.
Apply business logic filters:
Don't just concatenate chunks. Structure the context intelligently:
The user's query often isn't the best query for retrieval. Transform it:
Query expansion - Generate multiple phrasings of the same question. Retrieve for each and merge results.
Hypothetical Document Embedding (HyDE) - Have the LLM generate a hypothetical answer, then use that to retrieve. Often more effective than querying with the question directly.
Decomposition - Break complex questions into simpler sub-questions. Retrieve for each and synthesize.
Default chunking (split by tokens or characters) is rarely optimal.
Semantic chunking - Split at natural boundaries (paragraphs, sections) rather than arbitrary token counts.
Hierarchical chunking - Create multiple chunk sizes. Retrieve at the appropriate granularity for each query.
Overlapping chunks - Include context from adjacent chunks to preserve continuity.
Metadata enrichment - Attach document structure (headers, section titles) to each chunk for better context.
Vector search alone has limitations. Combine approaches:
BM25 + Vector - Traditional keyword search catches exact matches that semantic search misses. Fuse results from both.
Structured + Unstructured - If your documents have structured metadata (dates, categories, authors), use SQL-style filtering alongside vector search.
Knowledge Graphs + Vectors - For complex domains, extract entities and relationships into a knowledge graph. Use graph traversal to find related concepts, then vector search within that subspace.
You can't improve what you don't measure. Build evaluation into your RAG pipeline:
Retrieval metrics:
End-to-end metrics:
Create a test set. 50-100 representative queries with known-good answers. Run it regularly to catch regressions.
Caching - Cache embeddings, cache retrieval results for common queries, cache LLM responses where appropriate.
Latency - Optimize for perceived performance. Stream the LLM response while displaying retrieved sources.
Cost - Retrieval is cheap; LLM calls are expensive. Optimize context length. Consider smaller models for simple queries.
Monitoring - Log queries, retrieved documents, and generated answers. Build feedback loops for continuous improvement.
RAG is evolving rapidly:
The fundamentals matter most. Get retrieval right, and the rest follows.
Let's discuss how we can help bring your ideas to life with thoughtful engineering and AI that actually works.
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