Importance of Evaluations in AI Systems

Demos are easy; durability is hard. Samuel Colvin has spent a decade building guardrails in Python (first with Pydantic, now with Logfire), and he’s convinced most LLM failures have nothing to do with the model itself. They appear where the data is fuzzy, the prompts drift, or no one bothered to measure real-world behavior. Samuel joins me to show how a sprinkle of engineering discipline keeps those failures from ever reaching users.

We talk through:
• Tiny labels, big leverage: how five thumbs-ups/thumbs-downs are enough for Logfire to build a rubric that scores every call in real time
• Drift alarms, not dashboards: catching the moment your prompt or data shifts instead of reading charts after the fact
• Prompt self-repair: a prototype agent that rewrites its own system prompt—and tells you when it still doesn’t have what it needs
• The hidden cost curve: why the last 15 percent of reliability costs far more than the flashy 85 percent demo
• Business-first metrics: shipping features that meet real goals instead of chasing another decimal point of “accuracy”

If you’re past the proof-of-concept stage and staring down the “now it has to work” cliff, this episode is your climbing guide.

LINKS

• Pydantic
• Logfire
• Upcoming Events on Luma
• Hugo's recent newsletter about upcoming events and more!

🎓 Learn more:

• Hugo's course: Building LLM Applications for Data Scientists and Software Engineers — next cohort starts July 8: https://maven.com/s/course/d56067f338

📺 Watch the video version on YouTube: YouTube link