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:

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

Ready to Begin?

Start your journey with our first tutorial on structured outputs

Start Learning

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