Instructor — Structured LLM outputs validated with Pydantic
Instructor — Structured LLM outputs validated with Pydantic
Start with meaning, then move to detail.
This lesson explains Instructor — Structured LLM outputs validated with Pydantic as part of getting started with Hermes correctly. You will learn what it does, when it matters, and the smallest safe test that proves it works.
If you are new, do not memorize names. Focus on three questions: what problem does this solve, what access does it need, and how can you verify the result?
For practice, inspect the first example, identify its effects, run it on test data, and compare the result with the source claim.
For advanced readers, inspect Skill metadata, Reference: full SKILL.md, When to Use This Skill, then verify failure modes and version compatibility.
No prior experience is required; follow the steps on a safe test setup first.
A clear outcome before you read.
- Understand Instructor — Structured LLM outputs validated with Pydantic without assumed prior knowledge.
- Separate the source description from what still needs testing in your environment.
- Read the first command and identify its inputs and outputs before copying it.
Short definitions before the details.
- Provider
- The service that runs or provides access and authentication to a model.
- Skill
- An instruction bundle that teaches Hermes a repeatable workflow without necessarily adding an external service.
Structured LLM outputs validated with Pydantic
What does the source say, and in what order?
- 01Skill metadata
Start here to understand the core idea or structure.
- 02Reference: full SKILL.md
Read this after the foundation, then connect it to the previous step.
- 03When to Use This Skill
Read this after the foundation, then connect it to the previous step.
- 04Installation
Read this after the foundation, then connect it to the previous step.
- 05Quick Start
Read this after the foundation, then connect it to the previous step.
- 06Basic Example: Extract User Data
Read this after the foundation, then connect it to the previous step.
- 07With OpenAI
Read this after the foundation, then connect it to the previous step.
- 08Core Concepts
Read this after the foundation, then connect it to the previous step.
- 091. Response Models (Pydantic)
Read this after the foundation, then connect it to the previous step.
- 10Basic Model
Finish here to verify the result and special cases.
Copy only after you understand the effect.
# Base installation
pip install instructor
# With specific providers
pip install "instructor[anthropic]" # Anthropic Claude
pip install "instructor[openai]" # OpenAI
pip install "instructor[all]" # All providers### With OpenAI## Core Concepts
### 1. Response Models (Pydantic)
Response models define the structure and validation rules for LLM outputs.
#### Basic ModelRead the first command and identify its inputs and outputs before copying it.
Match every command to your installed Hermes version, review the files and accounts it can reach, and use non-sensitive data for the first test. If this explanation differs from the source, the official source wins.