Practical guides, tutorials, and reference documentation for building with Fiddler.Documentation Index
Fetch the complete documentation index at: https://handbook.fiddler.ai/llms.txt
Use this file to discover all available pages before exploring further.
⚡ Quick Starts
Get up and running in minutes with step-by-step quick start guides:- Get Started in <10 Minutes - Fastest way to integrate Fiddler
- Agentic Monitoring Quick Starts - Monitor AI agents and multi-step workflows
- Experiments Quick Start - Evaluate LLM outputs with custom metrics
- Guardrails Quick Start - Add safety guardrails to your AI applications
📚 Tutorials
In-depth, hands-on tutorials organized by product area:Experiments
Learn how to evaluate and test your LLM applications:Agentic & LLM Monitoring
Monitor production LLM applications and AI agents:Guardrails
Implement safety controls for your AI applications:ML Monitoring
Monitor traditional ML models in production:- ML Monitoring Quick Start
- NLP Model Monitoring
- Class Imbalance Handling
- Model Versions
- Ranking Models
- Regression Models
- Feature Impact Analysis
- Computer Vision Monitoring
🍳 Cookbooks
Use-case oriented guides that demonstrate end-to-end workflows for solving real problems:- RAG Evaluation Fundamentals — Evaluate RAG quality with built-in evaluators
- Running RAG Experiments at Scale — Compare pipeline configurations systematically
- Building Custom Judge Evaluators — Create domain-specific evaluation criteria
- Detecting Hallucinations in RAG — Monitor for hallucinations in production
- Monitoring Agentic Content Generation — Quality and brand compliance for content agents
📖 Client Library Reference
Comprehensive reference documentation for Fiddler’s Python client:Getting Started
Model Onboarding
Publishing Production Data
- Creating a Baseline Dataset
- Publishing Batches of Events
- Streaming Live Events
- Updating Events
- Deleting Events
- Ranking Events
Related Documentation
- SDK & API Reference - Complete API documentation
- Integrations - Connect Fiddler with your ML stack
- Documentation - Product guides and platform documentation