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Evaluation

Definition

Evaluation coverage in this archive spans 4 posts from Feb 2024 to Mar 2026 and treats evaluation as a production discipline: evaluation loops, tool boundaries, escalation paths, and cost control. The strongest adjacent threads are ai, llm, and quality. Recurring title motifs include ai, evaluation, model, and llm.

Key claims

  • The archive repeatedly argues that evaluation only creates leverage when it is wired into an existing workflow.
  • The consistent theme from 2024 to 2026 is disciplined execution over hype cycles.
  • This topic repeatedly intersects with ai, llm, and quality, so design choices here rarely stand alone.

Practical 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 llm before committing implementation details.

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 2024 to 2026 without revisiting assumptions as context changed.

Suggested reading path

References

3 entries tagged “Evaluation”

    Picking an AI Model for Production (Late 2024) · 5 min There's no best model. There's the model that fits your workload, latency budget, cost constraint, and ops tolerance. Here's how to compare them. ai models comparison How I Actually Test LLM Features · 6 min LLM outputs are non-deterministic. That doesn't mean you can't test them rigorously. Here's the layered testing approach I use in production. llm testing ai LLM Evaluation: Stop Shipping on Vibes · 5 min Your LLM feature looks great in demos and breaks in production. Here is how to build an evaluation loop that catches regressions before your users do. evaluation llm testing
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