Module 6: LLM APIs (Python)#
CodeVision Python Training
Contents#
Group 1: LLM API Fundamentals (Sections 6.1-6.5)
Group 2: Building LLM Clients (Sections 6.6-6.10)
Group 3: Structured Output & Validation (Sections 6.11-6.15)
Group 4: Reliability & Production Patterns (Sections 6.16-6.20)
Welcome to Module 6#
This module teaches you how to work with LLM APIs in Python, building robust clients that handle real-world challenges like network failures, malformed responses, and rate limits.
It is practical and code-focused, showing you how to build production-quality LLM integrations. By the end of this module, you will be able to call LLMs reliably and extract structured data from their responses.
This module builds directly on:
Module 1: Python fundamentals (classes, functions, JSON)
Module 3: LLM Fundamentals (prompting, inference)
Module 5: Embeddings & Vector Databases (retrieval patterns)
What You Will Learn#
Topic |
Why It Matters |
|---|---|
LLM API architecture |
Understand how LLM services work |
Building HTTP clients |
Connect to Ollama, OpenAI, and other APIs |
Structured output |
Get JSON instead of free-form text |
Response parsing |
Handle markdown-wrapped JSON and edge cases |
Retry with backoff |
Handle transient failures gracefully |
Schema validation |
Ensure responses match expected format |
Rate limiting |
Respect API quotas and avoid bans |
Error handling |
Build resilient production systems |
Prerequisites#
Before starting this module, ensure you have:
Completed Module 1 (Python Foundations)
Completed Module 3 (LLM Fundamentals)
Completed Module 5 (Embeddings & Vector Databases)
Module 6 Learning Path#
Content - Work through the interactive notebook
Quiz - Test your understanding (auto-graded)
Assessment - Coding tasks (LLM client, JSON parsing, retry logic, validation) + written explanation (auto-graded)
Resources - Additional learning materials
End of Module 6 Introduction#
Click Content in the navigation to begin the interactive lesson.