AI Agents & RAG with Python: Build 3 Real LLM Apps

Course Overview

Learn how to build production-ready AI agents and Retrieval Augmented Generation (RAG) applications with Python by developing three real large language model (LLM) projects. This course covers key concepts including RAG pipelines, vector search, autonomous agents, evaluation, security, and deployment in under four hours without requiring advanced AI or machine learning background.

You will gain practical skills to explain LLM internals, write robust prompts, build RAG systems, ship autonomous agents, and deploy AI applications securely and efficiently.

Key Takeaways

• Build production-ready RAG pipelines with chunking, embeddings, vector search, re-ranking, and grounded answers with citations.
• Design and deploy autonomous AI agents featuring tool calling, memory, planning loops, and multi-step reasoning.
• Develop three end-to-end LLM applications: a document Q&A assistant, a research agent, and a multi-agent workflow.
• Evaluate and test LLM apps using hallucination checks, retrieval metrics, regression tests, and prompt/version tracking.
• Deploy and operate LLM apps with FastAPI, Docker, streaming responses, caching, and cost/latency monitoring.
• Implement security measures to protect against prompt injection, data leakage, and runaway token costs using guardrails and rate limits.

Prerequisites

• Basic Python knowledge including functions, classes, and pip.
• Python 3.10+ installed on any OS (Windows, macOS, or Linux).
• An OpenAI or other LLM provider API key (free or low-cost usage is sufficient).
• Familiarity with command line and Git basics is helpful but not mandatory.

Target Learners

• Python developers transitioning from ChatGPT prompting to building production-grade LLM applications.
• Backend and full-stack engineers integrating RAG, chatbots, or agent features into existing products.
• Data scientists and ML engineers seeking practical LLMOps skills including evaluation, deployment, and monitoring.
• Students and career switchers aiming to build portfolio-worthy AI projects beyond toy demos.
• Technical leads assessing the feasibility and cost of deploying agents and RAG for business solutions.

Final Project

• Project 1: AI Content Repurposer – transforms one article into a summary, LinkedIn post, five tweets, an email, and a video script using a multi-stage pipeline with parallel generation and self-critique.
• Project 2: Chat With Your Own Documents – a full RAG application featuring structure-aware ingestion, hybrid retrieval, cross-encoder re-ranking, clickable page citations, honest refusal responses, and access control.
• Project 3: Autonomous Research Agent – an autonomous agent that plans research steps, calls APIs, recovers from failures, resists prompt injection, and logs a complete reasoning trace.