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:Documentation Index
Fetch the complete documentation index at: https://futureagi.mintlify.app/llms.txt
Use this file to discover all available pages before exploring further.
- 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