A Developer's Checklist for Every RAG Lifecycle (Beyond Chunk-Embed-Search)

A developer has published a checklist covering ten lifecycles of production-ready RAG systems, arguing that the common 'chunk-embed-search-LLM' model misses most of what matters. The checklist spans document, embedding, retrieval, inference, prompt, request, cache, evaluation, production, and cloud lifecycles, with practical checks such as updating single documents without full re-indexing, tracking tokens/sec, and measuring retrieval precision. The full technical breakdown with diagrams is available on Hashnode.

If your mental model of RAG is "chunk → embed → search → LLM," you're missing about 80% of what actually makes a RAG system production-ready. Here's a practical checklist across all 10 lifecycles I ran into while building one. Full technical breakdown with diagrams is on Hashnode linked above — this is the condensed, "what to actually check" version. ✅ Document lifecycle - Can you update a single document without a full re-index? - Do you have a deletion path not just an addition path ? - Are you deduplicating before you embed? ✅ Embedding lifecycle - Do you know what happens if you switch embedding models? - Are you tracking dimensions and normalization consistently? - Can you re-embed the whole store without downtime? ✅ Retrieval lifecycle - Are you tuning Top-K, or using a default and hoping? - Do you have metadata filtering before similarity search? - Have you tried hybrid keyword + semantic search yet? ✅ Inference lifecycle - Do you know your cold-start latency vs. warm inference? - Are you tracking tokens/sec as a real metric, not a vibe? - CPU or GPU — did you choose, or did it choose you? ✅ Prompt lifecycle - Are you compressing context, or dumping everything retrieved? - Do you track input vs. output tokens separately? - Is your system prompt fighting your retrieved context? ✅ Request lifecycle - Can you see latency broken down by stage embed / retrieve / generate ? - Do you know which stage is your actual bottleneck? ✅ Cache lifecycle - Are you caching query embeddings? - Are you caching full responses for repeated questions? ✅ Evaluation lifecycle - Can you measure retrieval precision/recall? - Do you have a faithfulness or answer-relevance check? - If you "improved" something, can you prove it? ✅ Production lifecycle - Health checks, retries, rate limiting — in place or assumed? - Are secrets actually out of your codebase? - Do you have CI/CD, or are you deploying by hand? ✅ Cloud lifecycle - Do you know your cost per query, end to end? - Is monitoring set up before something breaks, not after? If you're early in building RAG systems, you don't need to check every box on day one. But you should know every box exists — that's the difference between a demo and a system. Full write-up with architecture diagrams for each lifecycle: Hashnode link .