Fix Your Deployed AI System

If your deployed AI system is underperforming, the solution isn’t starting over it’s running a structured recovery process.

In this video, we walk through a 4-week AI recovery framework used by real teams to stabilize and improve production AI systems.

Week 1 — Stakeholder Alignment

Bring engineering, product, and business teams together to define measurable success criteria:

Accuracy targets
Latency expectations
Cost constraints
Unacceptable behaviors
Week 2 — Implement Tracing

Set up observability using tools like Langfuse, LangSmith, or Braintrust so every AI interaction becomes measurable and diagnosable.

Week 3 — Test Cases & Baseline

Create a regression test suite using real production examples labeled by domain experts to establish performance baselines.

Week 4 — Diagnose & Prioritize Fixes

Identify failure patterns, prioritize by business impact, validate improvements, and share results with stakeholders.

Most AI systems fail not because of models but because teams lack visibility and measurement.

This is how you turn a struggling AI deployment into a reliable production system.

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Видео Fix Your Deployed AI System канала AI in Production