Pydantic for LLM Workflows
by DeepLearning.AI
Validate LLM output, user input and tool arguments with Pydantic models in a customer support assistant.
Overview
Pydantic for LLM Workflows is a DeepLearning.AI short course taught by Ryan Keenan, Director of the Learning Experience Lab at DeepLearning.AI. It was announced in August 2025, and the course page lists a last update of October 13, 2026. The course has eight video lessons, six code examples and one graded quiz, listed at 1 hour 50 minutes in total. A 3-minute welcome and a 10-minute introduction lead into Pydantic Model Basics (13 minutes). Validating LLM Responses (15 minutes) applies validation to model output. Passing a Pydantic Model in Your API Call (9 minutes) sends the schema to the LLM provider. Tool Calling (19 minutes) is the longest lesson. A 5-minute project introduction leads into a 15-minute hands-on project, followed by a 7-minute quiz and a 10-minute appendix of tips and downloads. A customer support assistant runs through the course: the application needs complete, correctly formatted output for creating support tickets, triggering tools or routing requests. Across the lessons you validate user input such as malformed emails or missing fields, LLM responses and tool-calling parameters, and you use Pydantic models with LLM provider APIs and agent frameworks. Enrollment is free during the DeepLearning.AI learning platform beta, which the page describes as a limited-time offer. The graded assignment and the accomplishment require a PRO membership.
At a Glance
- Topic
- Frameworks
- Level
- Intermediate
- Format
- Course
- Cost
- Freemium
- Duration
- 1h 50m listed, self-paced
- Provider
- DeepLearning.AI
- Hands-on
- Yes — code/exercises
- Certificate
- Available
What You’ll Learn
- ✓Define Pydantic models that describe the data your LLM application expects
- ✓Validate LLM responses so malformed output is caught before other components use it
- ✓Check user input for problems such as malformed emails or missing fields
- ✓Pass a Pydantic model in API calls to LLM providers and agent frameworks
- ✓Validate tool-calling parameters with Pydantic models before a tool runs
- ✓Combine structured outputs and tool calling in one customer support assistant
Highlights
- •Tool Calling is the longest lesson at 19 minutes, and validated tool arguments are a large part of what the course teaches.
- •One customer support assistant runs through the lessons and the 15-minute hands-on project, so each validation technique is applied to the same application.
- •The course page lists an October 13, 2026 update. The course forum still shows a 'Broken code example: Tool calling' thread last active on December 24, 2025, so check that notebook when you reach it.
- •The narrow scope makes it a quick add-on for developers who already call LLM APIs. DataCamp's November 2026 Pydantic course roundup lists it as a free, short route to understanding structured output and function calling.
Who It’s For
Best For
- ✓Python developers whose LLM responses feed databases, APIs or downstream services
- ✓Engineers adding structured outputs and validated tool calls to an existing assistant
- ✓Learners who want a short introduction before using agent frameworks built on Pydantic
Prerequisites
- •Comfort with basic Python, as stated on the course page
- •No earlier Pydantic experience is listed as a requirement, and the course begins with Pydantic Model Basics
FAQ
What is Pydantic for LLM Workflows?
A short course for Python developers who need LLM output to feed other software reliably. Taught by Ryan Keenan of DeepLearning.AI, it covers Pydantic models, response validation, passing schemas in API calls and tool calling, and you finish by building a validated customer support assistant.
Is Pydantic for LLM Workflows free?
Pydantic for LLM Workflows offers free content, with paid options for certificates or premium features.
What level is Pydantic for LLM Workflows for?
Pydantic for LLM Workflows is aimed at a intermediate audience. Recommended background: Comfort with basic Python, as stated on the course page, No earlier Pydantic experience is listed as a requirement, and the course begins with Pydantic Model Basics.
How long does Pydantic for LLM Workflows take?
Expect roughly 1h 50m listed, self-paced. Most learners work through it at their own pace.
What will I learn from Pydantic for LLM Workflows?
You'll learn: Define Pydantic models that describe the data your LLM application expects; Validate LLM responses so malformed output is caught before other components use it; Check user input for problems such as malformed emails or missing fields; Pass a Pydantic model in API calls to LLM providers and agent frameworks; Validate tool-calling parameters with Pydantic models before a tool runs; Combine structured outputs and tool calling in one customer support assistant.
Topics
Sources
This page was written from 4 sources, 3 on domains other than deeplearning.ai.