Building a RAG System: Architecture, Retrieval, Quality, and Release Gates

This guide is a reading path for taking a knowledge hub RAG assistant from a demo into a releaseable system. It keeps the three RAG articles together as one guide: first define the system boundary, then implement retrieval, and finally decide whether quality, safety, and release evidence are strong enough to expose the assistant.

The guide fits personal knowledge hubs, documentation sites, and small public knowledge collections. It does not cover multi-tenant authorization, confidential internal documents, PII compliance processing, or enterprise audit reporting.

How to Read

Start with the architecture article if the runtime boundary, storage split, or index release flow is still unclear. Move to the retrieval article if the worker already exists but recall quality drifts. Read the quality and safety article before opening a public /chat entry point or running a new ingestion apply.

The Three Articles

1. RAG System Architecture: Edge Runtime, Hybrid Retrieval, and Incremental Indexing

   • Answers where the entry point, storage layers, model service, and pre-release index synchronization belong.
   • Focuses on Cloudflare Workers, Vectorize, D1 FTS5, KV, hybrid retrieval, and incremental indexing.
   • Best read while architecture boundaries and release scripts are still being frozen.

2. RAG Retrieval Implementation Deep Dive: Chunking, Hybrid Retrieval, and Intent Routing

   • Answers how to chunk content, recall evidence, route current-page summaries, and degrade safely when rerank fails.
   • Focuses on stable chunk IDs, Vectorize plus D1 FTS5 recall, RRF fusion, rule-based intent routing, and fallback behavior.
   • Best read when citations drift, exact signals are missed, or current-page questions leak into full-site answers.

3. RAG Quality Evaluation and Safety Controls: From Rule-Based Evaluation to Release Gates

   • Answers whether the answer is grounded, whether the request should continue downstream, and whether this version is safe to release.
   • Focuses on retrieval, citation, answer quality, fixed evaluation sets, safety gates, debug reports, closeout gates, and reverse audits.
   • Best read before exposing the assistant or treating an ingestion run as release-ready.

Core Questions

After reading the three articles, you should be able to answer:

• How should a knowledge hub RAG system divide static content, worker runtime, storage, model calls, and release scripts?
• How should chunking, hybrid retrieval, intent routing, and current-page summaries work together?
• What quality evidence and safety gates are required before a public assistant can be treated as release-ready?

Recommended Practice Order

1. Freeze the content boundary and export the corpus only from published dist output.
2. Generate stable documentId, documentHash, and chunk IDs for every document.
3. Keep vector recall and keyword recall together instead of relying on embeddings alone.
4. Separate current-page summaries, site-wide Q&A, and low-relevance fallback paths.
5. Use a fixed evaluation set for retrieval, citation, answer quality, fallback, and language behavior.
6. Run rag:export, rag:audit, dry-run ingest, apply, verify, and answer-quality evaluation before release.

Place in the AI Engineering Delivery Map

The RAG knowledge entry solves the question “is this answer grounded in site evidence?” It belongs beside AI-TDD, BMAD/Speckit, and delivery gates: RAG constrains knowledge boundaries, AI-TDD constrains acceptance contracts, and delivery gates turn results into reviewable evidence.