# Cookbooks

- [Overview](https://docs.fiddler.ai/developers/cookbooks/cookbooks.md): Use-case oriented guides for solving real-world AI evaluation and monitoring problems with Fiddler.
- [RAG Evaluation Fundamentals](https://docs.fiddler.ai/developers/cookbooks/rag-evaluation-fundamentals.md): Evaluate RAG application quality using Fiddler's built-in evaluators with direct scoring for rapid iteration on retrieval and generation quality.
- [Running RAG Experiments at Scale](https://docs.fiddler.ai/developers/cookbooks/rag-experiments-at-scale.md): Run structured RAG experiments with Datasets, golden label validation, and side-by-side comparison of pipeline configurations.
- [Building Custom Judge Evaluators](https://docs.fiddler.ai/developers/cookbooks/custom-judge-evaluators.md): Build domain-specific LLM-as-a-Judge evaluators using CustomJudge with prompt templates, structured output fields, and iterative prompt improvement.
- [Multimodal Evaluators](https://docs.fiddler.ai/developers/cookbooks/multimodal-evaluators.md): Build evaluators for document processing pipelines using CustomJudge with vision-capable models. Verify extraction accuracy and summarization faithfulness.
- [Detecting Hallucinations in RAG](https://docs.fiddler.ai/developers/cookbooks/hallucination-detection-pipeline.md): Build a complete hallucination detection pipeline combining Evals SDK evaluation with LLM Observability enrichments for continuous RAG monitoring.
- [Monitoring Agentic Content Generation](https://docs.fiddler.ai/developers/cookbooks/agentic-content-generation.md): Monitor agentic content generation for quality, safety, and brand compliance using built-in evaluators and custom LLM-as-a-Judge scoring.
- [Agentic Document Extraction](https://docs.fiddler.ai/developers/cookbooks/agentic-document-extraction.md): Build observable, measurable document extraction pipelines using Fiddler's agentic tracing, custom evaluators, and experiments.


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