AI Engineering with LLM APIs

Build production-ready AI applications by mastering LLM API integration with Python through this hands-on coding course. Learn essential skills including provider-agnostic interfaces, prompt engineering techniques, structured JSON responses with Pydantic validation, streaming responses, and multi-stage prompt chaining workflows. Develop robust applications with content moderation using Llama Guard, token management for cost optimization, and error handling patterns. Perfect for Python developers who want to integrate large language models into real-world applications.

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Course Chapters

1

LLM API Integration

Get started with Large Language Model APIs by understanding how LLMs work and setting up your development environment. Learn to configure the Codeling CLI, set up UV for Python package management, and obtain API keys to make your first LLM API calls.

3 lessons
2

Provider Agnostic Interface

Build flexible LLM integrations that work across multiple AI providers using Python design patterns. Master configuration management with Pydantic Settings, implement abstract base classes for provider interfaces, and use the Factory Pattern to create swappable AI provider implementations.

5 lessons
3

Roles and Randomness

Control LLM behavior through system roles and temperature settings for more predictable and tailored responses. Learn to load prompts from files, implement system roles to define AI persona and constraints, and configure temperature parameters to balance creativity versus determinism in model outputs.

5 lessons
4

Structured Responses and Prompting Techniques

Extract reliable structured data from LLMs using JSON responses, Pydantic validation, and advanced prompting strategies. Master data and prompt separation, implement zero-shot and few-shot prompting techniques, validate responses with Pydantic models, and build robust retry logic for handling API failures.

8 lessons
5

Prompt Chaining and Workflows

Build sophisticated AI workflows by connecting multiple LLM calls in sequence to solve complex problems. Learn prompt chaining concepts, create threat assessment models, implement multi-stage prompting pipelines, and orchestrate LLM calls where outputs from one stage inform the next.

4 lessons
6

Streaming Responses

Implement real-time streaming for responsive AI applications that display output as it's generated. Learn streaming fundamentals, add streaming methods to your provider interface, consume streamed responses chunk by chunk, build interactive chat interfaces, and handle stream errors gracefully.

7 lessons
7

Content Moderation

Build safe AI applications with layered content moderation strategies using local filters and LLM-based safety models. Create moderation result models, implement keyword-based local moderation, integrate Llama Guard for advanced content safety, and apply defense-in-depth patterns for production-ready moderation systems.

9 lessons
8

Token Management and Cost Optimization

Understand and optimize LLM costs by tracking token usage and applying efficient prompting strategies. Learn how tokenization works, extract token usage from API responses, track usage across streaming and non-streaming calls, understand context window limits, and apply best practices to reduce costs while maintaining quality.

7 lessons