Generative AI & RAG Engineering — Study Guide
An illustrated, exam-aligned study guide for building grounded LLM applications: prompting and decoding control, the end-to-end RAG pipeline (load, chunk, embed, index, retrieve, rerank, generate), vector databases and indexes, hybrid retrieval and reranking, hallucination mitigation and citations, RAG evaluation (faithfulness, relevance, RAGAS), and function calling with guardrails. Diagrams, comparison tables, and worked examples throughout.
Key facts
- Course price: $195 (one-time payment)
- Field: Artificial Intelligence & LLMs
- Prepares for: Generative AI & RAG Engineering
- 5 modules · 17 lessons · about 4 hours of lessons
- Self-paced, online, illustrated lessons
Course outline
- Prompting & Decoding Control — Steer an LLM at inference time: zero/few-shot and chain-of-thought prompting, system prompts, and the decoding dials — temperature, top-k, top-p.
- Prompting Techniques: Zero-shot, Few-shot, Chain-of-Thought
- System Prompts & Role Design
- Decoding Parameters: Temperature, Top-k, Top-p
- The RAG Pipeline End to End — The full architecture: offline ingest (load, chunk, embed, index) and online query (embed, retrieve, rerank, generate), plus chunking strategy.
- RAG Architecture: Ingest and Query
- Document Loading & Chunking Strategies
- Embeddings for Retrieval
- Vector Databases & Retrieval — Storing and searching embeddings: HNSW and IVF indexes, similarity metrics, and dense vs sparse vs hybrid retrieval with reranking.
- Vector Databases & ANN Indexes (HNSW, IVF)
- Similarity Metrics: Cosine vs Dot Product
- Dense, Sparse & Hybrid Retrieval + Reranking
- Grounding, Hallucination & Evaluation — Context-window assembly, hallucination causes and mitigation, citations, and measuring RAG quality with faithfulness, relevance, and RAGAS.
- Context-Window Management & Prompt Assembly
- Hallucination: Causes & Mitigation
- Citations & Grounding
- Evaluating RAG: Faithfulness, Relevance, RAGAS
- Tools, Structured Output, Guardrails & Cost — Function/tool calling, structured JSON output, guardrails and safety against prompt injection, and controlling latency and cost with caching.
- Function / Tool Calling
- Structured Output (JSON)
- Guardrails, Safety & Prompt Injection
- Caching & Cost Control