> ## Documentation Index
> Fetch the complete documentation index at: https://docs.evidentlyai.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Tutorials and guides
> End-to-end code examples.
**We have an applied course on LLM evaluations!** Free video course with 10+ tutorials. [Sign up](https://www.evidentlyai.com/llm-evaluation-course-practice).
## Quickstarts
If you are new, start here.
Evaluate the quality of text outputs.
Test tabular data quality and data drift.
Collect inputs and outputs from AI your app.
## LLM Tutorials
End-to-end examples of specific workflows and use cases.
How to create and evaluate an LLM judge against human labels.
A walkthrough of different RAG evaluation metrics.
Using multiple LLMs to evaluate the same output.
A walkthrough of different LLM evaluation methods. \[CODE + VIDEO]
A walkthrough of different descriptors (deterministic, ML, etc.) a single notebook.
Optimize a multi-class classifier using target labels.
Optimize a binary classifier using target labels and free-form feedback.
## ML tutorials
End-to-end examples of specific workflows and use cases.
Various data/ML metrics: Regression, Classification, Data Quality, Data Drift.
## Integrations
End-to-end examples of integrating Evidently with other tools and platforms.
Running Evidently evals as part of CI/CD workflow. Native GitHub action integration for regression testing.
Examples of using different external evaluator LLMs as LLM judges: OpenAI, Gemini, Google Vertex, Mistral, Ollama.
Visualize Evidently LLM evaluation metrics with Grafana. (Postgres as a database).
Visualize Evidently data drift evaluations on a Grafana dashboard. (Postgres as a database).
## Deployment
How to create a workspace, project and run Reports.
## LLM Evaluation Course - Video Tutorials
We have an applied LLM evaluation course where we walk through the core evaluation workflows. Each consists of the code example and a video tutorial walthrough.
📥 [Sign up for the course](https://www.evidentlyai.com/llm-evaluation-course-practice)
📹 [See complete Youtube playlist](https://www.youtube.com/watch?v=K8LLVi5Xrh8\&list=PL9omX6impEuNTr0KGLChHwhvN-q3ZF12d\&index=2)
| **Tutorial** | **Description** | **Code example** | **Video** |
| -------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- |
| **Intro to LLM Evals** | Introduction to LLM evaluation: concepts, goals, and motivations behind evaluating LLM outputs. | – | |
| **LLM Evaluation Methods** | Tutorial with an overview of methods. - Part 1. Anatomy of a single evaluation. Covers basic LLM evaluation API and setup.
- Part 2. Reference-based evaluation: exact match, semantic similarity, BERTScore, and LLM judge.
- Part 3. Reference-free evaluation: text statistics, regex, ML models, LLM judges, and session-level evaluators.
| [Open Notebook](https://github.com/evidentlyai/community-examples/blob/main/learn/LLMCourse_Tutorial_1_Intro_to_LLM_evals_methods.ipynb) | |
| **LLM as a Judge** | Tutorial on creating and tuning LLM judges aligned with human preferences. | [Open Notebook](LLMCourse_Tutorial_2_LLM_as_a_judge.ipynb) | |
| **Clasification Evaluation** | Tutorial on evaluating LLMs and a simple predictive ML baseline on a multi-class classification task. | [Open Notebook](https://github.com/evidentlyai/community-examples/blob/main/learn/LLMCourse_Classification_Evals.ipynb) | |
| **Content Generation with LLMs** | Tutorial on how to use LLMs to write tweets and evaluate how engaging they are. Introduction to the concept of tracing. | [Open Notebook](https://github.com/evidentlyai/community-examples/blob/main/learn/LLMCourse_Content_Generation_Evals.ipynb) | |
| **RAG evaluations** | - Part 1. Theory on how to evaluate RAG systems: retrieval, generation quality and synthetic data.
- Part 2. Tutorial on building a toy RAG application and evaluating correctness and faithfulness.
| [Open Notebook](https://github.com/evidentlyai/community-examples/blob/main/learn/LLMCourse_RAG_Evals.ipynb) | |
| **AI agent evaluations** | Tutorial on how to build a simple Q\&A agent and evaluate tool choice and answer correctness. | [Open Notebook](https://github.com/evidentlyai/community-examples/blob/main/learn/LLMCourse_Agent_Evals.ipynb) | |
| **Adversarial testing** | Tutorial on how to run scenario-based risk testing on forbidden topics and brand risks. | [Open Notebook](https://github.com/evidentlyai/community-examples/blob/main/learn/LLMCourse_Adversarial_Testing.ipynb) | |
## More examples
You can also find more examples in the [Example Repository](https://github.com/evidentlyai/community-examples).