AI Docs That Don't Lie to Your Users
Most AI documentation systems retrieve the wrong version, hallucinate details, and never admit uncertainty. Here's how to build one that actually helps.
Rag
Definition
Rag coverage in this archive spans 6 posts from Apr 2023 to Mar 2026 and treats rag as a production discipline: evaluation loops, tool boundaries, escalation paths, and cost control. The strongest adjacent threads are ai, llm, and go. Recurring title motifs include ai, ai-powered, knowledge, and management.
Working claims
- The archive repeatedly argues that rag only creates leverage when it is wired into an existing workflow.
- Early posts lean on patterns and production, while newer posts lean on ai and pipeline as constraints shifted.
- This topic repeatedly intersects with ai, llm, and go, so design choices here rarely stand alone.
How to apply this
- Define quality gates up front: eval sets, guardrails, and explicit rollback criteria.
- Start with the newest post to calibrate current constraints, then backtrack to older entries for first principles.
- When boundary questions appear, cross-read ai and llm before committing implementation details.
Where teams get burned
- Shipping agent behavior without hard boundaries for tools, data access, and approvals.
- Optimizing for model novelty while ignoring reliability, latency, or cost drift.
- Applying guidance from 2023 to 2026 without revisiting assumptions as context changed.
Suggested reading path
- Start here (current state): AI-Powered Knowledge Management
- Then read (operating middle): RAG Retrieval That Actually Works
- Finish with (foundational context): RAG Patterns That Actually Work in Production
Related posts
- AI-Powered Knowledge Management
- AI Docs That Don’t Lie to Your Users
- Your AI Pipeline Is Just ETL With Extra Steps (And That’s Fine)
- RAG Retrieval That Actually Works
- Stop Stuffing Your Context Window
- RAG Patterns That Actually Work in Production
References
Your AI Pipeline Is Just ETL With Extra Steps (And That's Fine)
AI data pipelines aren't some new paradigm. They're ETL with a retrieval layer bolted on. The discipline that makes them work is the same discipline that has always made pipelines work: detect change, chunk intelligently, keep indexes fresh.
RAG Retrieval That Actually Works
Most RAG failures are retrieval failures. Fixing them requires hybrid search, smarter chunking, query expansion, and reranking -- measured independently from generation.
Stop Stuffing Your Context Window
Bigger context windows aren't an excuse to stop thinking about what goes into them. Most teams are paying for irrelevant tokens and wondering why quality degrades.
RAG Patterns That Actually Work in Production
RAG is the default architecture for grounding LLMs in private data. Here are the patterns that survive real traffic, with Go examples from production systems.