How to Evaluate LLM Apps Before You Launch

📹 Synthetic Data Flywheels for LLM Apps

This is a recording of a live lightning lesson taught by Hugo Bowne-Anderson (AI builder, consultant, educator + host of Vanishing Gradients) and Nathan Danielson (AI Engineering Team Lead at Carvana).

In this session, they walk through how to apply evaluation-driven development to LLM apps — using synthetic data to build a minimum viable evaluation framework (MVE) before real users ever see your product.

You’ll learn how to:
• Generate synthetic user queries based on realistic personas
• Label outputs by hand to define correctness and failure modes
• Build an evaluation harness to compare models and prompts
• Use structured analysis to drive iteration and improvement
• Track accuracy, cost, and latency with lightweight observability

🧠 Join the Course

This lesson is part of the course:
Building LLM Applications for Data Scientists and Software Engineers

➡️ Learn more & apply to join the next cohort (starting April 7!): https://maven.com/s/course/d56067f338
💸 Use code GENAI200OFF for $200 off

📦 Repo + Slides

Want the repo and slides from this session?
Just fill out this short form and I’ll send them over:
https://forms.gle/Wg5prYkFLAJRbpDt8

00:00 Introduction to Synthetic Data Flywheels for LLM Apps
00:21 Building an Evaluation Harness for LLM Apps
01:06 Creating an Eval Driven Loop
01:38 Defining Personas and Generating Synthetic Questions
03:43 Understanding the Importance of Evaluation
06:19 Building a Minimum Viable Evaluation Framework (MVE)
10:22 Using LLMs and Manual Labeling for Evaluation
14:40 Practical Examples and Code Walkthrough
20:25 Iterating and Improving with Failure Analysis
27:08 Versioning and Observability in LLM Development
30:09 Course Information and Closing Remarks