RAG from Scratch: Build a Knowledge-Powered Chatbot

Descripción

Learn how to design, build, evaluate, and deploy production-quality Retrieval-Augmented Generation (RAG) systems for real-world enterprise use cases. You’ll start by understanding why LLMs hallucinate and how RAG differs from fine-tuning and long-context strategies. Then you’ll implement a complete naive RAG pipeline from scratch in Python: generating embeddings with OpenAI, computing cosine similarity, building a simple vector store, and wiring up retrieval-augmented prompts that produce grounded, cited answers. From there, you’ll progress to robust ingestion and retrieval pipelines for enterprise documentation. You’ll parse PDFs, Markdown, HTML, and CSVs into unified document objects; compare and tune chunking strategies; enrich chunks with metadata; and work with modern vector databases such as ChromaDB, FAISS, and Pinecone, including metadata filtering, namespaces, and multi-tenant access control. You’ll explore lexical (BM25), semantic, and hybrid retrieval with Reciprocal Rank Fusion, add cross-encoder and LLM-based reranking, and benchmark alternative pipelines using RAGAS metrics and synthetic test sets. Finally, you’ll focus on productionization: building a conversational Streamlit chatbot with conversation memory, query reformulation, and Agentic RAG workflows like query routing, self-corrective retrieval, and fallback strategies. You’ll compare advanced architectures such as Multi-Index RAG and GraphRAG, optimize for quality, latency, and cost through tuning and profiling, and deploy a secure, monitored enterprise RAG chatbot complete with streaming responses, authentication, user feedback loops, and automated re-indexing in the cloud. Disclaimer: This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated. The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.