567-labs--instructor/docs/tutorials/index.md

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Instructor Tutorials	Interactive, step-by-step tutorials for learning how to use Instructor effectively

Instructor Tutorials

• :material-school: Learning Path

  Follow our structured learning path to become an Instructor expert

  :octicons-arrow-right-16: Start Learning

• :material-notebook-edit: Interactive Formats

  Run our Jupyter notebooks in your preferred environment

  :octicons-arrow-right-16: Run Options

• :material-certificate: Skill Building

  Gain practical skills for real-world AI applications

  :octicons-arrow-right-16: What You'll Learn

• :material-help: Support

  Get help when you need it

  :octicons-arrow-right-16: Get Help

Tutorial Pathway

Our tutorials follow a carefully designed learning path from basic concepts to advanced applications. Each tutorial builds on previous concepts while introducing new techniques.

Tutorial	Topic	Key Skills	Difficulty
1. Introduction to Structured Outputs	Basic extraction	Pydantic models, basic prompting	🟢 Beginner
2. Tips and Tricks	Best practices	Advanced models, optimization	🟢 Beginner
3. Applications: RAG	Retrieval-augmented generation	Information retrieval, context handling	🟡 Intermediate
4. Applications: RAG Validation	Validating RAG outputs	Quality control, validation hooks	🟡 Intermediate
5. Validation Techniques	Deep validation	Custom validators, error handling	🟡 Intermediate
6. Knowledge Graphs	Graph building	Entity relationships, graph visualization	🔴 Advanced
7. Chain of Density	Summarization techniques	Iterative refinement, content density	🔴 Advanced
8. Synthetic Data Generation	Creating datasets	Data augmentation, testing data	🔴 Advanced

Running Options

Choose your preferred environment to work through these interactive Jupyter notebooks:

• :material-laptop: Run Locally

  git clone https://github.com/jxnl/instructor.git
  cd instructor
  pip install -e ".[all]"
  jupyter notebook docs/tutorials/
  

• :material-google: Google Colab

  Look for the "Open in Colab" button at the top of each notebook

  Perfect for cloud execution without local setup

• :simple-mybinder: Binder

  Click the "Launch Binder" button to run instantly in your browser

  No installation or API keys required for basic examples

Skills Gained

By completing this tutorial series, you'll gain practical skills in:

• Structured Extraction: Define Pydantic models that capture exactly the data you need
• Advanced Validation: Ensure LLM outputs meet your data quality requirements
• Streaming Responses: Process data in real-time with partial and iterative outputs
• Complex Applications: Build RAG systems, knowledge graphs, and more
• Multi-Provider Support: Work with different LLM providers using a consistent interface
• Production Techniques: Learn optimization strategies for real-world applications

Setup Requirements

Before starting, make sure you have:

• Python Environment: Python 3.8+ installed
• Dependencies: Install with pip install "instructor[all]"
• API Keys: Access to OpenAI API or other supported providers
• Basic Knowledge: Familiarity with Python and basic LLM concepts

Getting Help

We're here to support your learning journey:

• Documentation: Check the core concepts for detailed explanations
• FAQ: Browse our frequently asked questions
• Community: Join our Discord server for real-time help
• Issues: Report problems on GitHub
• Examples: See practical examples of Instructor in action

• :material-play-circle: Ready to Begin?

  Start your journey with our first tutorial on structured outputs

  :octicons-arrow-right-16: Start Learning{: .md-button .md-button--primary }