Integrating Model APIs with Python

Integrating Model APIs with Python

This course is part of Python for AI Product Builders Specialization

Instructor: Vasco Patrício

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2 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

You will learn to integrate LLM APIs with Python reliably, securely, and cost-effectively, from authentication and model selection to error handling
You will learn to build prompt-driven AI features using reusable templates, validated structured outputs, and retrieval-augmented generation

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This course is part of the Python for AI Product Builders Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 2 modules in this course

Calling an LLM API takes minutes. Making it reliable, secure, and affordable takes design. In this course, you will learn how to integrate model APIs with Python and build prompt-driven AI features that hold up in production.

You’ll learn how the API request and response cycle works, how to choose between raw HTTP and an SDK, and how to select a model by weighing capability, latency, cost, and risk. You’ll explore how message roles, generation parameters, and conversation history shape chat interactions, and how to handle errors with bounded retries while protecting API keys and controlling costs. The course also covers reusable prompt templates, structured outputs validated with Pydantic, and retrieval-augmented generation that grounds answers in private or current information. What makes this course unique is its focus on engineering judgment over tool tutorials. Not just calling the API, but making it dependable. By the end, you will be able to build model-powered features that are predictable, secure, and cost-aware.

Integrate LLM APIs in Python by selecting models, structuring chat requests and conversation history, handling errors with bounded retries, protecting API keys, and controlling token costs.

What's included

6 videos1 reading

Build prompt-driven AI features using versioned prompt templates, validated structured outputs with Pydantic, safe recovery from malformed responses, and retrieval-augmented generation.

What's included

6 videos1 reading1 assignment

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Instructor

Vasco Patrício

SkillsBooster Academy

7 Courses1,190 learners

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