Overview

Google, IBM & Meta Certificates – 40% Off

One plan covers every Professional Certificate on Coursera.

This course teaches a complete, production-oriented Retrieval-Augmented Generation (RAG) system — document ingestion, chunking, embeddings, vector search, summarization, RAG-based Q&A, evaluation, and deployment — through one evolving "AI Knowledge Assistant" project. The structure and pedagogy are strong: short 6–7 minute videos, a single running project, and a natural progression from raw PDFs to a deployed chat application. Overall Verdict: A technically sound, well-sequenced RAG engineering course that is roughly 75–80% aligned with 2026 industry practice. Its core weakness is that it teaches RAG as a closed pipeline rather than as one capability inside the broader 2026 agentic AI stack — Model Context Protocol (MCP), multi-agent orchestration (LangGraph/CrewAI), GraphRAG, and multimodal document understanding are absent or only implied. With targeted updates (not a rebuild), this course can be brought to full currency. 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.

Syllabus

Foundations, Document Ingestion & Text Processing

Understand core LLM and RAG concepts, set up the project environment, ingest documents from multiple formats, and prepare clean text through parsing, cleaning, chunking, and tokenization

Embeddings, Vector Search & Summarization

Generate embeddings, build and query a vector database, engineer effective prompts, and produce extractive and abstractive summaries at document and corpus scale.

Retrieval-Augmented Generation & Q&A

Build a complete RAG Q&A pipeline, add citations and memory, implement advanced retrieval and agentic patterns, and rigorously evaluate the system's answer quality.

Optimization, Deployment & Best Practices

Explore open-source and fine-tuning options, optimize cost and performance, deploy the system as an API and interactive chat dashboard, and apply industry best practices for production-ready GenAI applications