Step by step RAG evaluation using deepeval |Tutorial:127

github:https://github.com/ronidas39/LLMtutorial/tree/main/tutorial127 telegram: https://t.me/ttyoutubediscussion # Welcome to Total Technology Zone - Tutorial 127 Hey everyone, Roni here,welcome back to Total Technology Zone and to Tutorial 127! In this in-depth session, we’re diving into one of the most crucial steps in building robust generative AI applications: evaluating your Retrieval Augmented Generation (RAG) system using DeepEval by Confident AI. If you’ve been following our channel, you know how important it is to ensure that large language model outputs aren’t just interesting but also accurate, relevant, and faithful to your original source documents. In this comprehensive guide, I’ll walk you step by step through: 1. Generating “Golden” QA Data - How to transform a raw text (like a book or article) into a labeled dataset with question-answer-context sets. - Fine-tuning parameters such as `max_context_per_document`, `chunk_size`, and `max_golden_per_context` to balance granularity vs. token usage. - Handling errors and edge cases that arise when generating large sets of QA pairs. 2. Uploading Datasets into DeepEval - Creating or logging into your DeepEval account and grabbing your API key. - Seamlessly pushing your newly generated QA dataset so it can be versioned, tracked, and used for consistent, repeatable evaluations. - Understanding the DeepEval dashboard to monitor dataset health and coverage. 3. Constructing the RAG Pipeline with LangChain - Using ChromaDB for vector storage of embeddings. - Splitting large documents into manageable chunks with the `RecursiveCharacterTextSplitter`. - Employing OpenAI GPT-4 for LLM-based question-answering, and generating embeddings with `OpenAIEmbeddings`. 4. Defining and Using Evaluation Metrics - Why the choice of evaluation metrics matters for generative AI. - A deep dive into Answer Relevancy and Faithfulness—two pivotal metrics for QA-based systems. - How these metrics help you distinguish between “technically correct” answers and “hallucinated/irrelevant” ones. 5. Running Bulk Evaluations - Creating test cases that pair your model’s actual output with the expected output from your golden set. - Executing large-scale evaluations (e.g., 20, 50, 100+ questions) in parallel. - Interpreting pass/fail results and diagnosing where your RAG pipeline might need fine-tuning—like improving chunk sizes, prompt templates, or retriever configurations. 6. Practical Tips & Best Practices - Token usage and cost considerations: The bigger your dataset, the more tokens your system will consume. - Understanding how to handle ambiguous queries or out-of-scope questions in your RAG setup. - Strategies for iterative improvement, from refining prompts to updating source documents and retraining embeddings. --- Why Is This Tutorial Important? In an era of rapidly evolving AI tools, it’s no longer enough to generate text. Ensuring quality—through metrics and verifiable QA—makes your system dependable, credible, and production-ready. The DeepEval framework automates a large part of this evaluation, letting you focus on iterative improvements rather than manual checks. Useful Resources 1. DeepEval Framework - [Confident AI’s Official Website](https://www.confident-ai.com/) (Placeholder; use the actual DeepEval link if different.) - [DeepEval Documentation](https://docs.deepeval.ai/) 2. LangChain - [LangChain Official Docs](https://python.langchain.com/) - Tutorials on advanced usage, including how to integrate multiple retrieval methods. 3. OpenAI - [OpenAI GPT-4 Overview](https://openai.com/gpt-4) - [OpenAI Embeddings](https://platform.openai.com/docs/guides/embeddings) 4. Code Repository (if available) - Find the code snippets, environment details, and commented examples in our [GitHub repo]() or code Gist (link placeholder). --- Next Steps & Additional Learning - Fine-Tuning the Pipeline: Try different chunk sizes and overlap strategies to see if your answers become more or less faithful. - Prompt Engineering: Experiment with various prompt templates for more accurate retrieval. - Exploring Other Metrics: Delve into other advanced metrics like coherence or factual correctness for even deeper insights. --- Support This Channel If you found this tutorial helpful, please: 1. Give it a thumbs-up: It helps others discover the video. 2. Subscribe: Don’t miss out on our upcoming tutorials on advanced NLP, LLMs, and AI frameworks. 3. Share with friends & colleagues: If you know someone exploring RAG or AI evaluation, this video could be a game-changer! 4. Comment below: Let me know your thoughts, questions, or any specific challenges you’re facing. I’m always happy to help! #RAG #DeepEval #AI #LangChain #OpenAI #ChromaDB #LLMEvaluation #NLP #GenerativeAI #RetrievalAugmentedGeneration #DataScience