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Said Yaka

Topic

AI Engineering

11 essays

  1. Designing Human-in-the-Loop Approvals for Agents That Act

    Human-in-the-loop only works if people actually read the ask. Here is how I decide what needs approval, show the real action, and treat a no as final.

  2. Designing LLM Features Around Structured Outputs You Can Trust

    Treat the schema as the product contract. Here is how I design typed LLM outputs, validate and repair them, version the shape, and eval field by field.

  3. Designing Tools LLM Agents Can Actually Use

    Tool schemas are prompts. Here is how I name tools, shape parameters, return compact results, write recoverable errors, and eval whether agents can use them.

  4. Context Windows Are Not Memory

    A long context window feels like memory, but it is just a working set. Real memory needs selection, persistence, and a plan for what to forget.

  5. RAG Evaluations That Actually Catch Regressions

    A practical framework for building retrieval and answer-quality evaluations that catch real regressions instead of producing vanity metrics.

  6. Prompt Caching Is a Product Feature, Not Just an Infra Trick

    Why prompt caching changes latency, cost, and product design decisions — and why teams should treat it as a user-facing capability.

  7. What Agent Observability Should Actually Show You

    A practical view of agent observability: what to log, what to surface, and what teams need to understand before agents become trustworthy in production.

  8. How I Think About Model Routing in Production

    A practical way to think about model routing across cheap, fast, and strong models without turning your stack into a chaotic science project.

  9. MCP Feels Like the Missing UI Layer for Agents

    Why the Model Context Protocol matters: it gives agents a cleaner way to discover tools, carry context, and interact with software systems.

  10. When Not to Use RAG

    RAG is powerful, but it is not a default answer to every AI product problem. Here are the cases where I would avoid it or simplify it.

  11. Synthetic Data for LLM Evals: Useful, but Easy to Abuse

    Synthetic data can speed up AI evaluation pipelines, but only if it is used with strong guardrails and real-world calibration.