Implementing RAG: Production Patterns for AI Knowledge Bases

RAG (Retrieval-Augmented Generation) lets you build AI systems that reference your own documents. Instead of relying solely on the LLM’s training data, you RAG retrieves relevant documents and uses them as context for generation. This enables AI systems that can access current, specific information without hallucination.

How RAG Works

User Query → Embedding Search → Retrieve Top-K Documents → Combine Query + Documents → LLM Generation → Response

Retrieval Strategies

Strategy	Implementation	Accuracy	Latency
Semantic search	Vector embeddings + High	Low-Medium
Keyword search	Full-text or BM25	Medium	Very Low
Hybrid	Keyword + Rerank	High	Medium
Dense retrieval	All documents, context	Highest	High

Embedding Approaches

Method	Chunk Size	Trade-offs
Fixed-size	512 tokens	Fast, but context boundaries
Semantic	Sentence/paragraph	Slower, better boundaries
Recursive	Variable	Most accurate, most expensive

Production Considerations

Challenge	Solution
Latency	Cache embeddings, use smaller models for retrieval
Cost	Batch embedding, use smaller chunks
Freshness	Incremental updates, periodic reindexing
Hallucination	Cite sources, use lower temperature
Accuracy	Hybrid search, human feedback loop

Common Pitfalls

Pitfall	Symptom	Fix
Retrieving too much	Context exceeds window	Limit retrieval size, summarize
Poor chunking	Chunks split semantic meaning	Use semantic chunking
Stale embeddings	Outdated information	Incremental updates, expiration
No source attribution	Can’t verify information	Include source metadata in context
Over-engineering	Complex retrieval for simple queries	Start simple, add complexity as needed

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RAG enables AI systems to access your specific knowledge without hallucination. If you’re building an AI feature that needs to reference your documents, book a consultation. We’ll help you design a RAG system that actually works in production.