This section outlines a structured, evaluation-driven approach to refining LLM application performance. It explains how users can test, validate, and compare different prompt configurations, datasets, and evaluation methods to achieve consistent and reliable AI-generated outputs.This section covers:
What is experimentation.
Why experimentation is necessary.
Key benefits of systematic AI evaluation and improvement.
How experimentation works, from defining test cases to deploying refinements.