The AI Engineering Review

The situation

Release candidate 2.0 of Code Nexus Assist reads beautifully. In the demo its answers were warmer, fuller and better written, and the room loved it. The dashboard is less enthusiastic: it is less grounded than the version in production, slower, and more expensive per answer. Before anyone decides anything, the team wants a proper AI engineering review — what the report actually shows, what it doesn't measure, whether the candidate can be improved inside its limits, and what a release decision would need to rest on.

What you'll learn

• AI-103 across all domains: planning and quotas, retrieval and search health, vision, language and speech, extraction, agents and memory, and security — plus monitoring for quality, latency, cost and drift, held-out evaluation, prompt and configuration tuning, failure analysis, staged release, and recording model, index and prompt versions.
• Evaluate reflection and self-critique against source-supported claims, latency and token usage; trace model/tool calls. The programme practice bank is not a claim of current vendor exam proportions or external readiness.

Who you work with

• 

  Maya Chen

  Senior AI Engineer · Your mentor

• 

  Priya Naidoo

  Cloud Architect

• 

  Amara Okafor

  ML and Data Engineer

Scenes

1. 1.It reads beautifully
2. 2.What the report says, and what it doesn't
3. 3.Improve it inside its limits
4. 4.The monitor that would have told us
5. 5.The dashboard was green
6. 6.Ship, hold, or something in between
7. 7.Make it check its own work
8. 8.The assistant platform case study
9. 9.AI Engineering practice checkpoint

What you leave with

You inspected fictional release metrics, tuned against a development fixture and wrote a monitoring plan for quality, cost and freshness. Repeated tuning requires a separate final test. The release recommendation closes mandatory security/freshness checks in isolation before live rollout, with version records, tested fallback and stop criteria.