Overview - Fiddler Documentation

Cookbooks are use-case oriented guides that demonstrate end-to-end workflows for solving real problems with Fiddler. Unlike quick starts (which introduce product features) or tutorials (which deep-dive into specific capabilities), cookbooks are organized by scenario — they show you how to combine multiple Fiddler features to achieve a practical goal.

Prerequisites for all cookbooks

Fiddler account with API access
Python 3.10+
Fiddler Evals SDK: pip install fiddler-evals
LLM credential configured in Settings > LLM Gateway

RAG Evaluation & Monitoring

Cookbook	Use Case	Key Features
RAG Evaluation Fundamentals	”I have a RAG app and want to evaluate its quality”	RAG Faithfulness, Answer Relevance, direct `.score()` API
Running RAG Experiments at Scale	”I want to compare RAG pipeline configurations”	Datasets, Experiments, `evaluate()`, golden label validation
Detecting Hallucinations in RAG	”I want to monitor my RAG app for hallucinations”	RAG Health triad, Evaluator Rules, LLM Observability enrichments

Custom Evaluators

Cookbook	Use Case	Key Features
Building Custom Judge Evaluators	”I need domain-specific evaluation criteria”	`CustomJudge`, prompt templates, `output_fields`, iterative improvement

Agentic AI

Cookbook	Use Case	Key Features
Monitoring Agentic Content Generation	”I want to ensure quality and brand compliance in content generation agents”	Built-in evaluators + custom Brand Voice Match judge

Experiments Getting Started — Product overview
Experiments Quick Start — SDK setup and first experiment
RAG Health Diagnostics — Conceptual guide to the diagnostic triad
Evals SDK Advanced Guide — Advanced patterns

Guardrails Quick Start

RAG Evaluation Fundamentals

⌘I

​RAG Evaluation & Monitoring

​Custom Evaluators

​Agentic AI

​Related Resources

RAG Evaluation & Monitoring

Custom Evaluators

Agentic AI

Related Resources