Build RAG Powered AI App| Question → SearchDocuments → FormatContext → BuildPrompt → AIRequest → Answer| Step-by-Step Coding & Explanation | Part4
📚 Welcome back to the RAG AI series! In this part, we dive into the heart of Retrieval-Augmented Generation (RAG) — building the RAG Chain itself.
We'll walk through step by step code along with simple explanation:
1. How to connect the vector database (Pinecone) to a retriever
2. How to format search results as context
3. How to build a custom prompt template
4. How to use Google Gemini 1.5 Pro to answer queries based on real document data
5. Verified LLMs is not hallucinating when it does not know the answer with context given
6. Verified LLMs is providing response based on the real time data through which question was contextualized by retrieving from vector database
🎯 By the end of this video, you'll understand exactly how your RAG system retrieves, prompts, and responds — the real intelligence behind it!
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