MASTER SERIES - RAG 6 - QUERY PROCESSING AND OUTPUT GENERATION

Description

📘 Description In this video, we’ll dive deep into how Query Processing and Output Generation work in Retrieval-Augmented Generation (RAG) — one of the most powerful frameworks behind modern AI systems like ChatGPT, Gemini, and Claude. You’ll learn how user queries are understood, how relevant information is retrieved from external sources, and how the final response is generated by the language model — combining retrieval accuracy with generative intelligence. Perfect for AI enthusiasts, data scientists, and developers exploring LLMs, RAG pipelines, and vector databases. 🧩 What You’ll Learn 🔹 What is Query Processing in RAG? 🔹 How LLMs interpret user intent and embedding-based search 🔹 The role of vector databases in document retrieval 🔹 Output Generation: Combining retrieved context with language model responses 🔹 Example of an end-to-end RAG pipeline 💻 Hands-On Demo (if applicable) ✅ Query embedding and similarity search ✅ Context injection into the model ✅ Response generation using a retriever + generator framework (e.g., LangChain or LlamaIndex) 🎯 Ideal For AI & Machine Learning Students NLP Researchers and Practitioners Data Engineers building RAG pipelines Developers integrating LLMs with custom data 🧠 Key Takeaways Understand the workflow: Query → Retrieve → Generate → Output Learn how retrieval quality impacts answer accuracy See how to optimize RAG pipelines for enterprise applications 🔗 Watch Next How Retrieval-Augmented Generation Works Explained Simply Building a RAG App Using LangChain and FAISS Improving RAG Accuracy with Prompt Engineering 🎓 Hashtags #RAG #RetrievalAugmentedGeneration #GenerativeAI #LLM #AI #LangChain #VectorDatabase #PromptEngineering #OpenAI #ArtificialIntelligence #MachineLearning