Retrieval-Augmented Generation (RAG): concepts and vocabulary | Complete Generative AI: From Foundations to Production
A self-paced 26-week learning path with 15 modules: AI foundations, Python, machine learning, transformers, LLMs, prompts, APIs, embeddings, RAG, frameworks, agents, customization, multimodal AI, Spring Boot with React, and production LLMOps. Includes 15 practical assignments and three portfolio projects.
Prerequisites: Begin with basic computer skills. Programming is introduced in Weeks 2–3. Use a local Python environment for coding labs; Java and React experience helps in Weeks 21–22. Model downloads, provider accounts and deployment resources are optional learner-managed dependencies. The 26-week schedule is suggested; reading estimates exclude independent project work.
Learning goals
RAG architecture and ingestion
PDF, Word and web processing
Chunking and retrieval
Reranking and hybrid retrieval
Grounded answers and citations
RAG evaluation and hallucination reduction
Access-controlled retrieval
Understand the foundations
RAG retrieves relevant evidence and supplies it to a generator at inference time. Ingestion extracts text, preserves provenance, chunks content and computes representations. Query-time retrieval selects eligible passages; reranking can reorder candidates before a bounded context is built. Generation should answer only what the evidence supports and cite the passages used. Retrieval can reduce unsupported answers but cannot guarantee factuality, freshness or faithful citation on its own.
Connect the ideas
PDFs may require OCR, Word documents contain structural elements and web content can include navigation or hostile instructions. Limit file size and parser resources and retain page or section locations for citation. Enforce tenant and document permissions from trusted server identity, never a model-produced tenant field. Apply access filtering before reranking and prompt assembly, and recheck revoked permissions where needed. A citation must refer to a source the user may access and actually support the claim it accompanies.
Checkpoint: explain these concepts in your own words, then describe one failure that the design must detect. Use synthetic or permissioned data throughout the course.
01. Introduction to Artificial Intelligence · Week 1
02. Python Programming for Gen AI · Weeks 2–3
03. Mathematics and Machine Learning Foundations · Week 4
04. Deep Learning and Transformers · Weeks 5–6
05. Large Language Models (LLMs) · Weeks 7–8
06. Prompt Engineering · Weeks 9–10
07. Generative AI APIs and Application Development · Week 11
08. Embeddings and Vector Databases · Weeks 12–13
09. Retrieval-Augmented Generation (RAG) · Weeks 14–15
10. LangChain, LlamaIndex and AI Frameworks · Week 16
11. AI Agents and Agentic AI · Weeks 17–18
12. Fine-Tuning and Model Customization · Week 19
13. Multimodal Generative AI · Week 20
14. Gen AI with Java Spring Boot and React · Weeks 21–22
15. Production Gen AI, LLMOps and Capstone · Weeks 23–26