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Retrieval, prompting, avaliação, fine-tuning — a camada de engenharia que transforma um modelo de chat em um produto de produção.
Every piece, assembled: ingestion, hybrid retrieval, re-ranking, grounded generation, guardrails, eval and caching — the blueprint you can ship.
A RAG query touches embeddings, a vector DB, a re-ranker and an LLM. Each adds milliseconds and cents. At scale, discipline here is the difference between a margin and a bonfire.
When retrieval comes up empty, a helpful model invents. Guardrails turn 'confidently wrong' into 'honestly unsure' — the difference users actually trust.
Without an eval set, every RAG change is a vibe. With one, you tune chunking, retrieval and prompts with a number that tells you if you helped or hurt.
Great retrieval is wasted if the model ignores it or can't point to its sources. Grounding is a prompt-design discipline, not an afterthought.
Retrieval gets you 30 plausible chunks. A re-ranker reads them against the actual question and floats the truly relevant few to the top.