An AI evaluation is easy to trust when it confirms the release you already want. The harder test is whether it still helps after the prompt, model, tool, or traffic mix changes.
The 12-week Evaluation and AI Reliability roadmap begins below the evaluation layer. It covers enough model mechanics, probability, and descriptive statistics to explain what a metric measures and what it misses. It then moves into LLM evaluations, coding-agent benchmarks, and tool-use evaluations.
That foundation matters because different tasks fail differently. Exact match may fit a constrained output. A tool-using agent needs checks for action selection, arguments, side effects, recovery, and final evidence. A coding benchmark can reward a patch that passes a narrow test while missing the requested behavior.
The middle four weeks move from a local score to a production evaluation system. Prompt versions are explicit. Regression tests use frozen cases. Hallucination and failure detection become named behaviors. Quality, cost, and latency are measured together, because a release that improves one dimension can quietly damage another.
The final phase adds agent observability, tracing and replay, evidence-backed verification, and human review. The synthesis artifact is not a leaderboard. It is an evaluation and observability system that can reproduce a failure, compare a candidate with a baseline, and show where human judgment still enters.
A useful evaluation should make a bad release harder to justify. It should also make a good release easier to explain without stretching one number into a general claim.
The complete roadmap is at https://learn.significanthobbies.com/curriculum/roadmaps/ai-reliability-12w.
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