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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.

Concept

Learn the fundamentals of AI experimentation

How To

Step-by-step guides for running experiments