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.
Search
Definition
Search coverage in this archive spans 3 posts from Jun 2023 to Jul 2025 and treats search as a production discipline: evaluation loops, tool boundaries, escalation paths, and cost control. The strongest adjacent threads are ai, embeddings, and go. Recurring title motifs include ai, docs, users, and embedding.
What the archive argues
- The archive repeatedly argues that search only creates leverage when it is wired into an existing workflow.
- The consistent theme from 2023 to 2025 is disciplined execution over hype cycles.
- This topic repeatedly intersects with ai, embeddings, and go, so design choices here rarely stand alone.
Execution checklist
- 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 embeddings before committing implementation details.
Common failure modes
- 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 2025 without revisiting assumptions as context changed.
Suggested reading path
- Start here (current state): AI Docs That Don’t Lie to Your Users
- Then read (operating middle): Embedding Models Compared: What Actually Matters for Retrieval
- Finish with (foundational context): Building Semantic Search in Go: From Embeddings to Production
Related posts
- AI Docs That Don’t Lie to Your Users
- Embedding Models Compared: What Actually Matters for Retrieval
- Building Semantic Search in Go: From Embeddings to Production
References
Embedding Models Compared: What Actually Matters for Retrieval
I benchmarked five embedding models against the same retrieval task. The results challenged some of my assumptions about model size, cost, and quality.
Building Semantic Search in Go: From Embeddings to Production
A hands-on walkthrough of building semantic search with Go, OpenAI embeddings, and pgvector. Includes chunking strategies, hybrid retrieval, and the gotchas I hit along the way.