Your AI Product Needs Evals

2024-03-29 · Source: Hamel Husain's Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Intermediate, long

Summary

Hamel Husain, an independent consultant and former CodeSearchNet team lead, identifies the lack of robust evaluation systems as the primary reason for unsuccessful LLM products. He outlines a three-tiered evaluation framework for AI products, exemplified by the real estate AI assistant "Lucy" from Rechat. This framework includes Level 1 unit tests for rapid, cheap assertions, Level 2 human and model-based evaluations requiring trace logging and custom data viewing tools, and Level 3 A/B testing for mature products. The article emphasizes that a strong evaluation system not only enables faster iteration but also provides "superpowers" for free, such as streamlined data synthesis and curation for fine-tuning, and efficient debugging capabilities. Rechat, for instance, employs hundreds of unit tests, uses LangSmith for trace logging, and built custom Shiny for Python tools for human evaluation.

Key takeaway

For AI Engineers and MLOps Engineers building LLM-powered products, prioritizing a systematic evaluation framework is crucial. You should establish Level 1 unit tests for rapid feedback, integrate Level 2 human and automated model evaluations by logging traces, and consider Level 3 A/B testing for mature products. This approach will accelerate iteration, streamline debugging, and provide high-quality data for fine-tuning, preventing common development plateaus.

Key insights

Robust evaluation systems are critical for iterating quickly and improving LLM-powered AI products beyond initial demos.

Principles

• Iterate quickly for AI product success.
• Evaluation systems enable fine-tuning and debugging.
• Remove all friction from data inspection.

Method

Implement a three-level evaluation system: Level 1 unit tests for fast assertions, Level 2 human/model eval with trace logging, and Level 3 A/B testing for mature products. Continuously update tests and track results.

In practice

• Use LLMs to generate synthetic test cases.
• Build custom data viewing and labeling tools.
• Track model-human evaluation agreement.

Topics

• LLM Evaluation Systems
• LLM System Improvement
• Unit Testing
• Human-in-the-Loop Evaluation
• LLM Fine-Tuning

Code references

• github/CodeSearchNet
• traceloop/openllmetry

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Hamel Husain's Blog.