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Instructor Tutorials

  • Learning Path

    Follow our structured learning path to become an Instructor expert

    Start Learning

  • Interactive Formats

    Run our Jupyter notebooks in your preferred environment

    Run Options

  • Skill Building

    Gain practical skills for real-world AI applications

    What You'll Learn

  • Support

    Get help when you need it

    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:

  • Run Locally

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

  • 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:

  • Ready to Begin?

    Start your journey with our first tutorial on structured outputs

    Start Learning