AI & LLM Integration

Build AI-powered features: LLM API integration, prompt engineering, structured output, RAG patterns, and error handling — the must-have skill of 2026.

6 exercises

hardPython

RAG Text Retrieval with Cosine Similarity

Build a Retrieval-Augmented Generation (RAG) system that finds the most relevant text chunks for a given query. ## Requ...

raginformation-retrievalcosine-similarity+7

mediumPython

LLM Structured Output with Schema Validation

Build a function that enforces structured JSON output from a language model by validating responses against a schema and...

json

mediumPython

API Retry Wrapper with Exponential Backoff

Build a retry wrapper that handles failed API calls with intelligent retry logic. Your `RetryWrapper` class should: - A...

retry-logic

mediumPython

Text Chunking Pipeline with Overlap and Metadata

Build a text chunking pipeline that intelligently splits documents into overlapping chunks while preserving sentence bou...

text-processing

mediumPython

Prompt Template Engine

Build a `PromptTemplate` class that renders template strings with variable substitution. Your class should: - Take a te...

templates

easyPython

Mock LLM API Client with Error Handling

Build a function that calls a mock LLM API with proper error handling for common API issues. Create a function `call_ll...

api