Retrieval & grounding
RAG Architecture Explained
Beyond the basic concept: the real components, decisions, and failure points in a working RAG pipeline.
Understanding RAG conceptually is one thing. Building a version that performs well on real documents and real user questions requires understanding each component in the pipeline and the decisions that most affect quality.
This breaks a production RAG architecture into its core stages: ingestion, retrieval, and generation, with the specific choices that separate a mediocre RAG system from a genuinely reliable one.
Key takeaways
Ingestion: preparing your knowledge base
Documents must be cleaned, split into chunks, and embedded before they are searchable. Chunk size is a critical, easy-to-get-wrong decision: chunks too large dilute relevance, chunks too small lose context.
Good ingestion also preserves metadata — source, date, section — so the system can filter, cite, and reason about where an answer came from later.
Retrieval: finding the right context
Pure vector similarity search is a reasonable baseline but often misses exact terms, names, or codes that keyword search catches easily. Hybrid search — combining both — plus a reranking step that reorders results by true relevance, consistently outperforms vector search alone.
This retrieval stage is where most real-world RAG quality problems actually live, far more often than in the final generation step.
Generation: using context responsibly
The final prompt should clearly instruct the model to answer only from the provided context and to say so explicitly when the context does not contain an answer, rather than filling gaps with invented information.
Well-designed generation prompts dramatically reduce hallucination risk even when retrieval occasionally returns imperfect or incomplete context.
Put this into practice