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Build a real RAG Evaluation System with Python, LangChain, OpenAI, DeepEval, and uv. In this video, you'll learn how to evaluate a Retrieval-Augmented Generation system instead of just building another chatbot and hoping the answers are good. We'll build a practical evaluation workflow around a PDF knowledge base. The system extracts text from a PDF, creates embeddings, stores the chunks in an in-memory vector store, retrieves relevant context for each question, generates an answer with an OpenAI model, and then scores the result across multiple RAG evaluation metrics. The project uses a small question-and-answer dataset about penguins so we can clearly see what the RAG system retrieved, what the model answered, what the expected answer was, and how each metric scores the result. Instead of relying on vibes, we save structured JSONL outputs, evaluate each test case, calculate averages, measure pass rates, and compare experiments. By the end, you'll have a portfolio-ready RAG evaluation project that shows how to test answer quality, retrieval quality, faithfulness, and factual correctness in a real AI engineering workflow. What you'll learn How to build a PDF-based RAG system in Python How to extract text from a PDF with PyMuPDF How to create embeddings with OpenAI How to use a LangChain in-memory vector store How to build a retrieval tool for a RAG agent How to generate answers with a structured AI agent How to create an evaluation dataset with questions and expected answers How to save RAG outputs to JSONL How to evaluate RAG answers with DeepEval How to measure answer relevancy How to measure faithfulness to retrieved context How to measure contextual relevancy How to measure correctness with GEval How to compare multiple RAG experiments How to summarize evaluation results with averages and pass rates Project features PDF knowledge base Paragraph extraction pipeline OpenAI embeddings LangChain vector search RAG agent with retrieval tool Structured answer output 20-question evaluation dataset Expected answer comparison Saved RAG outputs in JSONL Saved evaluated results in JSONL DeepEval metric scoring Experiment summary files Retriever comparison workflow Reusable evaluation scripts Evaluation metrics Answer relevancy: Does the answer address the user's question? Faithfulness: Is the answer supported by the retrieved context? Contextual relevancy: Did the retriever return useful context? Correctness: Is the answer factually correct compared to the expected answer? Project architecture PDF document - extract paragraphs - generate embeddings - store chunks in vector store - ask evaluation questions - retrieve relevant context - generate RAG answer - save actual answer and context - build DeepEval test cases - run evaluation metrics - save scored results - summarize averages and pass rates - compare experiment versions Tech stack Python 3.12+ LangChain LangChain OpenAI OpenAI DeepEval PyMuPDF Pydantic python-dotenv uv JSONL VS Code GitHub Why this project matters Most RAG tutorials stop once the application can answer a question. But in real AI engineering, the more important question is: how do you know your RAG system is actually good? This project shows how to move from a simple retrieval demo to a repeatable evaluation workflow. You will see how to separate answer quality from retrieval quality, why retrieved context matters, how metric scores expose weaknesses, and how changing retrieval settings can affect the final system. This is the kind of project you can add to your AI engineering portfolio because it demonstrates: RAG system design LLM evaluation Retrieval evaluation Faithfulness testing Expected answer benchmarking Structured experiment tracking JSONL data pipelines Metric-based iteration LangChain integration OpenAI model usage DeepEval evaluation workflows Backend AI engineering in Python If you're learning RAG, LangChain, LLM evaluation, AI agents, backend engineering, or how to build production-minded AI applications in Python, this project is a strong next step after your first chatbot. Timeline 00:00 - Intro 04:05 - Project setup 08:54 - RAG creation 1:08:16 - Evaluation system creation 2:38:30 - Data-driven decision and RAG improvement 3:12:00 - Final project walkthrough Links below Full video: https://youtu.be/n_Oc-Wq_KqI?si=lMnjS8P9Sgfu4n32 Related video: https://youtu.be/J6qc9F8yklk?si=h6fkL0_V6Ahht-un GitHub Repo: https://github.com/Mohamad-Hachem/LLM_Evaluation_System #AI #RAG #RetrievalAugmentedGeneration #LLMEvaluation #DeepEval #LangChain #OpenAI #Python #AIAgents #ArtificialIntelligence #PromptEngineering #BackendEngineering #SoftwareEngineering #AIProjects #PortfolioProject #PythonDeveloper #VectorDatabase #Embeddings

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