• Format — 1,500+ MCQs with instant explanations
• Duration — about 17 hours of focused practice, typically 3–5 weeks at 40–60 questions a day
• Level — beginner to advanced; LLM Foundations and Prompt Engineering recommended first
• Cost — free, with a public-URL completion certificate
• Audience — engineers building document QA, semantic search, or enterprise RAG; AI Engineer interview candidates; builders moving from prototype-RAG to production-RAG
• Previous modules — LLM Foundations, Prompt Engineering

Three audiences land on this page. Working software engineers and data scientists who have built a RAG prototype that demos well on five PDFs and now need to scale it — handle a million chunks, deal with stale embeddings, hit p95 latency budgets, support multi-tenant access, defend against indirect injection from retrieved content. Final-year engineering and MCA students targeting AI Engineer interviews in 2026, where "design a RAG system for X" is now a standard system-design question and the bar is concrete production fluency, not LangChain tutorials. Builders and ML engineers who have used RAG libraries but never opened the box on what chunking, embedding, and reranking are actually doing under the hood — and want the conceptual library to debug systems that quietly fail.

Foundations

• Why RAG Exists — the limitations of pure LLMs (knowledge cutoff, hallucination on facts, private-data invisibility, context-window-vs-corpus tradeoffs); what RAG adds (grounded answers, citations, freshness, private knowledge); RAG vs long-context stuffing, fine-tuning, and tool calling
• RAG Pipeline Overview — the indexing phase (document loading, chunking, embedding generation, vector store ingestion, metadata enrichment); the query phase (query embedding, similarity search, context assembly, generation, post-processing); the common failure surfaces (retrieval misses, irrelevant chunks, context overflow, ignored context)
• Chunking Strategies — six chunking methods (fixed-size, recursive, sentence-aware, semantic, markdown-structure-aware, contextual chunking); chunk-size tradeoffs (small vs large, overlap, embedding-model token caps); special handling for tables, code blocks, long paragraphs, and lists

Indexing and retrieval

• Embeddings for Retrieval — model choices (OpenAI text-embedding-3, sentence-transformers, BGE and E5 families, domain-specific, multilingual); embedding properties (dimension and recall, max input tokens, normalization, asymmetric query-document); quality issues (lexical-semantic gap, domain mismatch, OOV jargon)
• Similarity Search — distance metrics (cosine in retrieval scoring, dot product on normalized vectors, euclidean); approximate nearest neighbor (HNSW graph, IVF inverted file, PQ product quantization, recall-latency tradeoff); hybrid retrieval (BM25, dense-plus-sparse fusion, RRF, when hybrid beats dense)
• Query Transformation — rewriting (synonym expansion, HyDE hypothetical-document embeddings, step-back prompting, subquery decomposition); multi-query retrieval (paraphrasing, score aggregation across variants); conversational handling (context-aware rewriting, coreference, history truncation, follow-up detection)
• Re-Ranking and Filtering — cross-encoder rerankers (bi vs cross encoder, Cohere rerank, MS-MARCO models, latency cost); metadata filtering (filter-then-retrieve, permission and tenant, date and recency); diversity (MMR with lambda tradeoff, near-duplicate removal, source diversity)

Production and Interview readiness

• Generation with Context — RAG prompt templates (answer-only-from-context, citation requirement, "I don't know" fallback, context formatting); context-window management (truncation, ordering, lost-in-the-middle mitigation, chunk compression); citation and attribution (inline markers, source ID mapping, verification, no-source-no-answer)
• RAG Evaluation — retrieval metrics (recall@k, precision@k, MRR, NDCG); generation quality metrics (faithfulness to context, answer relevance, context precision and recall, the RAGAS framework); end-to-end evaluation (ground-truth QA pairs, LLM-as-judge for RAG, hallucination detection, regression test sets)
• Advanced RAG Patterns — graph and structured RAG (knowledge graphs, GraphRAG community summaries, SQL RAG, structured-plus-unstructured fusion); agentic and iterative (self-RAG with critique, corrective RAG, iterative retrieval with reasoning); specialised variants (table-aware, code, long-document hierarchical index, multi-tenant)
• Production Concerns — latency and cost (embedding cost at scale, vector-DB query latency, rerank budget, retrieval caching); freshness and updates (incremental indexing, deletion and reindex, stale-embedding detection, background reindex); security (tenant isolation, PII in chunks, audit logging, indirect-injection defence on retrieved content)
• RAG Mastery — pattern comparisons (naive vs advanced RAG, dense vs hybrid, single-hop vs multi-hop, RAG vs fine-tuning); when-to-use selection (hybrid retrieval, rerankers, GraphRAG, long context vs RAG, combining RAG with fine-tuning); RAG in practice (customer support KBs, legal QA, code documentation, financial reports, internal company search)

Prompt Engineering teaches the patterns that go into a single LLM call — few-shot, chain of thought, structured output, ReAct, prompt injection defence. RAG Systems teaches what happens around that call when the model needs external knowledge to answer at all. Prompt Engineering is necessary for RAG (every RAG prompt is itself an engineered prompt — answer-only-from-context, citation requirement, "I don't know" fallback), but it's not sufficient. Building a RAG system means designing chunking, embedding choice, distance metric, ANN index, hybrid retrieval, reranking, context-window management, and end-to-end evaluation — none of which is covered by prompting alone. Most learners take Prompt Engineering first; this course assumes that grounding.

Most RAG systems that reach production hit a small set of recurring anti-patterns. This course is designed to surface and correct them through targeted MCQs. Among the most common:

• More retrieved chunks doesn't mean a better answer — beyond a threshold, context-window pollution and lost-in-the-middle work against you.
• A bigger embedding model doesn't mean better retrieval — domain mismatch matters more than model size, and a domain-specific small model often beats text-embedding-3 on technical corpora.
• Cosine similarity and dot product only match on normalized vectors — on raw embeddings they rank differently and can flip your top-k ordering.
• RAGAS scores measure faithfulness to retrieved context, not factual accuracy — a high RAGAS score on bad context is still a wrong answer.
• Hybrid retrieval is not always better — for purely semantic queries on clean text it adds latency without measurable recall gain.
• Rerankers don't increase recall, only precision — they re-order candidates that were already retrieved; if the right chunk wasn't in the candidate set, no reranker can rescue it.
• "Just bump the context window" doesn't replace retrieval — long-context models still suffer lost-in-the-middle, and attention cost scales quadratically.
• GraphRAG isn't a magic upgrade — it works for entity-rich corpora (legal, biomedical, regulatory) but underperforms vector RAG on narrative text.
• Embeddings drift silently — a model swap or domain shift can invalidate your entire index with no error signal until users complain.
• Indirect prompt injection via retrieved content is the dominant RAG attack surface in 2026 — untrusted-source labelling matters more here than direct-injection defence.

Each of these has a dedicated cluster of practice questions in the curriculum.

Build something, then break it. Most engineers learn RAG by following a LangChain tutorial and stopping there — and ship a system that demos well on five PDFs but quietly fails on a corpus of a million chunks. The Abekus format puts you on the hook for an answer every 40 seconds across the full production-RAG surface: chunking choices, embedding tradeoffs, ANN index parameters, reranking decisions, eval methodology, drift detection, security. The AI guide tracks which subtopics you keep missing and re-surfaces them in later sessions, so weak spots compound less.

One question at a time. Pick an answer. If you are wrong, the explanation appears immediately — usually a paragraph that walks through the mechanism (why does HNSW's ef parameter affect recall this way? why does the MMR lambda flip the diversity-relevance balance?), names the concept by its standard term, and points at the related labels in the curriculum. The AI guide watches your accuracy by subtopic and prioritises the weak ones in the next practice session. There is no video, no scrolling lecture — just focused retrieval practice on the patterns you actually need to ship a production RAG system.

The honest math: 1,500+ MCQs at about 40 seconds each (including reading the explanation on the wrong ones) is roughly 17 hours of pure focus. Spread that over 40–60 questions a day and you finish in about 3–5 weeks. At 80 questions a day, about 3 weeks. The course is designed for short ad-hoc sessions of 20–30 minutes — you do not need to block off a weekend. Most learners finish all four modules of the Generative AI Engineering track in 3–4 months at this pace.

The mastery topic at the end mirrors the RAG system-design questions that actually get asked in AI Engineer technical screens in 2026 — "design a RAG system for a legal-document QA product", "walk me through how you'd debug a RAG system whose accuracy dropped 15% after a model upgrade", "explain when GraphRAG beats vector RAG". The RAG Evaluation topic prepares you for the eval round (faithfulness vs accuracy, RAGAS, LLM-as-judge biases, regression test design). The Production Concerns topic prepares you for the "how would you scale this to N tenants" round. The In-Practice labels (customer support KBs, legal document QA, code documentation search, financial reports, internal company search) cover the scenario-based prompts that come up in product-engineering rounds.

The final module in the track is AI Agents — the natural next step. Many RAG patterns (corrective RAG, self-RAG, iterative retrieval with reasoning) overlap with agentic patterns, and the boundary between "advanced RAG" and "simple agent" in 2026 is mostly cosmetic. Finishing all four modules of the Generative AI Engineering track unlocks the series certificate. Independently, learners targeting Data Scientist roles often pair RAG Systems with the Statistics for Data Science and Machine Learning Foundations courses on Abekus.