Overview

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.