RAG Chatbot — Knowledge Base-Grounded AI Assistant

I will build a Retrieval-Augmented Generation (RAG) chatbot connected to your own knowledge base — covering document ingestion and chunking, vector embedding and storage, semantic retrieval at query time, and LLM response generation grounded in your retrieved content with source citation. A RAG chatbot answers questions accurately from your actual documentation, product manuals, or FAQ content rather than relying on the LLM's general training knowledge — eliminating the hallucination risk that makes a generic GPT chatbot unsuitable for answering specific, factual questions about your business.

The build covers document ingestion from your knowledge base (PDFs, Word docs, URLs, or plain text), chunking strategy and embedding generation, vector store setup (Pinecone, Chroma, or FAISS), retrieval pipeline with relevance scoring, LLM response synthesis using only retrieved content, source citation in responses, and a chat widget deployable on your website.

Designed for businesses wanting an AI assistant that answers questions accurately and specifically from their own content — support documentation, product information, or internal knowledge — without hallucinated or generic responses.