E2E Testing ChatBot, AI Agent, RAG, MCP Server with DeepEval

AI-powered applications are reshaping the software landscape — but how do you test them? Traditional QA methods fall short when your application thinks, reasons, and responds dynamically. This course bridges that gap. In this comprehensive, hands-on course, you'll learn how to build a complete end-to-end testing strategy for modern AI systems — including ChatBots, AI Agents, Retrieval-Augmented Generation (RAG) pipelines, and MCP Servers — using DeepEval, the leading open-source LLM evaluation framework. Every concept is grounded in a real-world e-commerce AI chatbot application, so you're always testing something meaningful, not toy examples. Course covers following Section 1 — Getting Started with DeepEval Section 2 — Running Local LLMs with Ollama Section 3 — LLM-as-a-Judge with Local Models Section 4 — Testing Real LangChain Applications Section 5 — Core Building Blocks: Test Cases, Datasets & Goldens Section 6 — Various Different Metrics + Custom Metrics Section 7 — Application Under Test (AUT) Section 8 — End-to-End Testing with Pytest + DeepEval Section 9 — Advanced Pytest Patterns & Automation Section 10 — Testing Conversational ChatBots Section 11 — Testing RAG Systems Crash Course - PyTest Framework Basic to Advanced Why This Course? As AI systems move into production, the demand for engineers who can evaluate and validate LLM-powered applications is growing fast. This course gives you practical, job-ready skills using real tools on a real application — not just theory. By the end, you'll have a complete, professional-grade evaluation framework you can apply to any AI project you work on. Tools & Technologies DeepEval · Pytest · Python · Ollama · LangChain · Jupyter Notebooks · FastAPI · Confident AI · GitHub Actions