8bit.tr Journal
Retrieval-Augmented Generation (RAG): Architecture, Pitfalls, and Best Practices
A practical guide to building RAG systems that are accurate, fast, and easy to maintain in production.
RAG Is a System, Not a Prompt
Retrieval-augmented generation connects a language model to external knowledge. The goal is simple: ground answers in real data while keeping responses fluent.
Most RAG failures are not model failures. They are indexing, retrieval, or query formulation problems that silently break relevance.
Indexing: Build for Retrieval, Not for Storage
A vector index should reflect how users ask questions. Chunk size, overlap, and metadata matter more than raw document count.
If your chunks are too large, retrieval becomes noisy. If they are too small, the model loses context. Start with 300 to 800 tokens and adjust based on quality.
Retrieval Quality Drives Answer Quality
Use hybrid retrieval when possible: combine dense vectors with keyword filters or BM25. This improves precision for structured and numeric content.
Add metadata filters for product areas, permissions, or freshness. The fastest way to improve quality is to reduce irrelevant context.
Prompting Is the Final Layer
Once retrieval is strong, the prompt should instruct the model to cite sources and refuse to answer if context is missing.
Avoid dumping everything into the prompt. Prioritize the top-ranked chunks, and enforce a strict citation format so users can verify results.
Operational Concerns in Production
RAG systems need monitoring. Track retrieval hit rate, answer correctness, and latency. A slow index is a slow product.
Plan for data updates. Stale embeddings degrade trust quickly. Automate re-indexing and keep versioned snapshots for rollback.
Quality Checklist for RAG
Treat RAG quality like a pipeline. Validate that the top retrieved chunks are relevant, recent, and permission-safe before they ever reach the model. Build a small evaluation set of real user questions and inspect the retrieved context for each one. If the context is weak, the answer will be weak no matter how good the model is.
Close the loop with user feedback. Track when users click sources, ask follow-up questions, or abandon the result. These signals point to missing documents, poor chunking, or overly broad queries. Adjust retrieval settings first, and only then tune prompts so you do not mask a retrieval problem with clever wording.
FAQ: RAG Systems
When should I use RAG? Use it when accuracy depends on up-to-date or proprietary knowledge.
Do I still need fine-tuning? Often no. Good retrieval plus a clean prompt can outperform a fine-tuned model for knowledge tasks.
What is the main failure mode? Irrelevant chunks entering the prompt, leading to confident but wrong answers.
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