Construire un système RAG prêt pour la production : Guide avancé

Why RAG Matters

Retrieval-Augmented Generation (RAG) is the most practical way to ground LLM responses in your own data. But building a production-ready system requires more than just connecting a vector database.

Chunking Strategies

• Fixed-size: Simple but may split semantic units.
• Semantic: Split by meaning using embedding similarity.
• Recursive: Split by headers, paragraphs, then sentences.
• Document-aware: Respect Markdown/HTML structure.

Hybrid Search

Combine dense vector search with sparse keyword search (BM25). This catches both semantic matches and exact term matches.

Re-ranking

After initial retrieval, use a cross-encoder model to re-score results. This significantly improves precision.

Evaluation

• Faithfulness: Does the answer stick to retrieved context?
• Relevance: Are the retrieved chunks actually useful?
• Answer correctness: Is the final answer factually correct?

Tools & Frameworks

• LlamaIndex: High-level RAG framework with built-in recipes.
• LangChain: Flexible orchestration layer.
• RAGAS: Purpose-built evaluation framework.
• Chroma / Qdrant / Weaviate: Vector databases with different trade-offs.