# Tutorials

- [Agentic & LLM Monitoring](/developers/tutorials/llm-monitoring.md)
- [LangGraph SDK Advanced](/developers/tutorials/llm-monitoring/langgraph-sdk-advanced.md): Advanced observability patterns for LangGraph applications including multi-agent workflows, conversation tracking, and production configuration.
- [Experiments](/developers/tutorials/experiments.md): Master LLM and AI application experiments with comprehensive tutorials covering the Fiddler Evals SDK, custom evaluators, model comparison, and custom experiment creation.
- [RAG Health Metrics Tutorial](/developers/tutorials/experiments/rag-health-metrics-tutorial.md): Step-by-step guide to evaluating RAG applications using the RAG Health Metrics diagnostic triad: Answer Relevance, Context Relevance, and RAG Faithfulness.
- [Advanced Prompt Specs](/developers/tutorials/experiments/prompt-specs-advanced.md): Advanced guide to Fiddler's LLM-as-a-Judge capabilities, including custom prompting, model selection, performance optimization, and enterprise deployment patterns.
- [Evals SDK Advanced Guide](/developers/tutorials/experiments/evals-sdk-advanced.md): Advanced experiment patterns for production LLM applications including multi-score evaluators, complex parameter mapping, and comprehensive experiment analysis.
- [Guardrails](/developers/tutorials/guardrails.md)
- [Faithfulness](/developers/tutorials/guardrails/guardrails-faithfulness.md): This Quick Start notebook introduces Fiddler Guardrails, an enterprise solution that safeguards LLM applications from risks like hallucinations, toxicity, and jailbreaking attempts.
- [Safety](/developers/tutorials/guardrails/guardrails-safety.md): This Quick Start Notebook introduces Fiddler Guardrails' Safety Detection capabilities, an essential component of our enterprise solution for protecting LLM applications.
- [PII](/developers/tutorials/guardrails/guardrails-pii.md): Learn to detect and protect PII, PHI, and sensitive data in text using Fiddler's fast PII guardrail for comprehensive privacy compliance.
- [ML Monitoring](/developers/tutorials/ml-monitoring.md)
- [NLP Inputs](/developers/tutorials/ml-monitoring/simple-nlp-monitoring-quick-start.md): Dive into our guide on using Fiddler to monitor NLP models. Learn how a multi-class classifier is applied to the dataset and monitored with Vector Monitoring.
- [Class Imbalance](/developers/tutorials/ml-monitoring/class-imbalance-monitoring-example.md): Discover how Fiddler uses class weighting to address class imbalance. Compare two identical models–with and without weighting–to detect drift signals.
- [Model Versions](/developers/tutorials/ml-monitoring/ml-monitoring-model-versions.md): Explore our guide to using Fiddler’s sample data to set up and manage multiple versions of a model with the powerful Model Versions feature.
- [Ranking Models](/developers/tutorials/ml-monitoring/ranking-model.md): Explore our notebook to see how Fiddler monitors ranking models using a public dataset organized around “search result impressions” from Expedia hotel searches.
- [Regression](/developers/tutorials/ml-monitoring/ml-monitoring-regression.md): Check out our guide on using Fiddler to evaluate regression models. See examples of detecting issues using data drift and performance metrics like MAE.
- [Feature Impact](/developers/tutorials/ml-monitoring/user-defined-feature-impact.md): Leverage this guide on using Fiddler's feature impact upload API to supply your own feature impact values for your Fiddler model.
- [CV Inputs](/developers/tutorials/ml-monitoring/cv-monitoring.md): Explore our guide to using Fiddler’s monitoring for computer vision models. Learn to detect drift in image data with our unique Vector Monitoring approach.