𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗥𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗥𝗔𝗚 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗥𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗥𝗔𝗚 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀

Most teams build RAG prototypes in a weekend. Few make them work in production. The problem is not the model. It is engineering.

Bad chunking ruins your results. Use hierarchical chunking.

• Use child chunks for precision.
• Use parent chunks for context.
• Add metadata like source IDs and content hashes.

Vector search alone is not enough. Use hybrid retrieval.

• Combine vector similarity with BM25 keywords.
• Use Reciprocal Rank Fusion to merge results.
• Use a cross-encoder re-ranker for precision.

Skipping the re-ranker is a big mistake. Initial retrieval finds many results. The re-ranker picks the best ones.

Stop hallucinations with grounding.

• Tell the model to admit when it lacks context.
• Force the model to cite sources for every claim.

Stop blaming the model for bad answers. Most failures happen during retrieval.

• Measure Recall@K.
• Measure MRR against a ground truth set.
• Fix retrieval first.

Your pipeline needs observability. Track these signals:

• I don't know rate.
• Chunks dropped rate.
• Retrieval latency.
• Corpus staleness.

Read the full guide for architecture diagrams and Python code.

Source: https://dev.to/aloknecessary/building-reliable-rag-pipelines-from-prototype-to-production-2mcp

Optional learning community: https://t.me/GyaanSetuAi