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AI engineering
AI engineering by D. Klincharski, focused on useful agent interfaces, measurable token efficiency, and answers grounded in source material. Explore MCP design notes, retrieval experiments, and the products that put these ideas to work.
About D. KlincharskiArticles / Experiments
Notes from the work.
01
Sep 02, 2026
Why AI gets regulatory affairs wrong when you remove the source documentsLanguage models are excellent at sounding like a regulation and unreliable at being one. Here is what goes wrong when the primary source is out of the loop, and how to keep it in.
02
Aug 09, 2026
Bedrock Knowledge Base Chunking Strategies: How to Choose (and What Changed in 2026)A practical decision guide to Bedrock Knowledge Base chunking, the managed-versus-customer-managed fork, starting parameters, evaluation, and Terraform.
03
Jul 28, 2026
MCP v2 Migration Guide: What Changed and Should You Upgrade?A practical MCP v2 migration guide covering SDK changes, the stateless 2026 protocol, compatibility risks, and when upgrading is worth it.
04
Jul 28, 2026
Should Every MCP Server Ship with an Agent Skill?An MCP server must stand on its own. Learn when an optional agent skill adds real value—and when it only hides a poor interface.
05
Jul 28, 2026
15 Rules for Designing Token-Efficient MCP APIs for AI AgentsFifteen practical rules for MCP APIs that reduce tool calls, context bloat, latency, and cost without starving AI agents of useful data.
06
Jul 24, 2026
Why domain expertise belongs in the product loopAI can accelerate regulated work, but useful software begins when domain specialists shape the product itself.
07
Jul 21, 2026
What RafiHive is for, and who actually needs itRegulatory work drowns in changing requirements and lost sources. RafiHive keeps the intelligence traceable so specialists can trust the answer.
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