RAG Pipeline Design

Who it's for

Engineers and AI builders whose agent or chatbot gets document content wrong, cites passages that don't exist, or can't find what's plainly in the knowledge base — and who've been grinding on the prompt for days with no improvement.

What you get

A retrieval-architect workflow that fixes the actual bottleneck:

• Corpus profile — understand what you're retrieving over before choosing tactics.
• Chunking strategy — so the correct answer can actually fit into a retrievable chunk.
• Embedding + vector-DB index selection — matched to your corpus and query shape.
• Retrieval strategy — top-k, hybrid BM25 + dense, metadata filters, rerank, query rewrite.
• Context assembly with citations — get the right chunk into the window, attributable.
• Golden Q&A set + metrics — recall@k, MRR, faithfulness, so every wrong answer is attributed to retrieval vs generation.
• A deliverable RAG design doc.

Why it's different

• Retrieval quality is the ceiling on answer quality — no prompt fixes a missing chunk.
• "Measure recall before you touch the prompt."
• "Hallucination from docs is usually a retrieval miss wearing a costume."
• A repeatable metric to judge whether retrieval actually works — not vibes.

Good starts

• "My chatbot can't find what's clearly in the docs."
• "RAG keeps hallucinating from our knowledge base."
• "Help me design chunking + retrieval for my agent."
• "How do I eval retrieval — recall@k, rerank, hybrid search?"

Languages

The setup, working steps, and output follow the language you type — usable in English, 中文, 日本語, and other languages the model supports.