RAG Done Right
Best practices for implementing Retrieval-Augmented Generation in production systems.
Retrieval-Augmented Generation has become the go-to technique for grounding LLMs in your data. But there's a vast difference between a RAG demo and a production RAG system. The details matter: chunking strategy, embedding choice, retrieval accuracy, and context management all determine whether your system actually works.
Chunking Is Everything
The way you split your documents determines retrieval quality. Too small, and you lose context. Too large, and you dilute relevance. There's no one-size-fits-all answer, it depends on your content and use case.
We've found that semantic chunking, splitting on natural boundaries like paragraphs and sections, works better than fixed-size chunks. ARES includes intelligent chunking that respects document structure while maintaining meaningful context windows.
Beyond Simple Vector Search
Vector similarity search is just the starting point. Production systems often need hybrid retrieval: combining semantic search with keyword matching, metadata filtering, and recency weighting.
Think about queries like "latest financial report" or "policy update from legal". Pure semantic search might miss the temporal or categorical requirements. Hybrid approaches catch what vector search alone misses.
RAG Best Practices
Reranking for Precision
Vector search is fast but imprecise. Embedding models compress documents into fixed-size vectors, losing nuance along the way. Reranking models look at query-document pairs directly, giving you much better precision.
The pattern: retrieve more candidates than you need with vector search, then rerank to find the truly relevant ones. This two-stage approach balances speed with accuracy.
Context Window Management
You've retrieved relevant chunks, but how do you fit them into the model's context window? Simply stuffing everything in wastes tokens and can actually hurt response quality, models can get lost in too much context.
Smart context assembly matters: deduplicate overlapping chunks, prioritize the most relevant pieces, and structure the context so the model can easily navigate it. ARES handles this automatically, maximizing retrieval value within token limits.
Evaluation and Iteration
How do you know if your RAG system is actually working? You need evaluation, and not just vibes. Track retrieval precision, answer accuracy, and user feedback. Build test sets that cover your real use cases.
The best RAG systems are built iteratively. Start simple, measure what works and what doesn't, then add complexity where it helps. Don't over-engineer before you understand your data and users.
Build Better RAG
ARES includes production-ready RAG pipelines with semantic chunking, hybrid search, and intelligent context management. Start building systems that actually work.
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