BC Skills Academy

Generative AI & RAG Engineering

Engineer grounded, production-grade LLM applications with retrieval

Key facts

About the Generative AI & RAG Engineering certification

Master the engineering discipline behind production generative-AI systems built on large language models. This professional certification starts with how you steer a model at inference time: prompting techniques (zero-shot, few-shot, chain-of-thought), system prompts and role design, and the decoding parameters — temperature, top-k, top-p — that trade determinism for diversity. The core of the course is Retrieval-Augmented Generation (RAG) end to end: loading and chunking documents (size, overlap, and semantic strategies), embedding text for retrieval, storing vectors in a vector database with the right index (HNSW, IVF), and measuring similarity with cosine and dot product. You will go deep on retrieval itself — dense vectors, sparse/BM25 lexical search, hybrid fusion, and reranking with cross-encoders — then assemble a context window that respects token budgets and avoids the lost-in-the-middle failure mode. The final third is what separates demos from products: grounding answers with citations, diagnosing and mitigating hallucination, evaluating a RAG pipeline for faithfulness and answer relevance (RAGAS), wiring function/tool calling and structured JSON output, adding guardrails and safety filters, and controlling cost with caching. By the end you can design, build, evaluate, and debug a grounded RAG application and defend every architectural choice against the alternatives.

Course outline

The Generative AI & RAG Engineering — Study Guide course covers:

  1. 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. (3 lessons)
  2. 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. (3 lessons)
  3. Vector Databases & Retrieval — Storing and searching embeddings: HNSW and IVF indexes, similarity metrics, and dense vs sparse vs hybrid retrieval with reranking. (3 lessons)
  4. Grounding, Hallucination & Evaluation — Context-window assembly, hallucination causes and mitigation, citations, and measuring RAG quality with faithfulness, relevance, and RAGAS. (4 lessons)
  5. Tools, Structured Output, Guardrails & Cost — Function/tool calling, structured JSON output, guardrails and safety against prompt injection, and controlling latency and cost with caching. (4 lessons)

Prerequisites

Frequently asked questions

Is the Generative AI & RAG Engineering certificate verifiable?
Yes. Every certificate issued by BC Skills Academy carries a unique credential ID that anyone can check on the public verification page.
How is the Generative AI & RAG Engineering exam structured?
It is a timed, randomized multiple-choice exam of 66 questions in 90 minutes, graded instantly; you need 75% to pass.
Can I get a refund?
Full refund within 7 days of purchase if you have not started the exam. Once the exam has been started or completed, the purchase is no longer refundable.

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