CoreIntermediate

LLM Engineering & RAG

Eight weeks building retrieval-backed LLM systems that hold up on real documents and real questions. You will build a working RAG system, measure it honestly, and learn the three failure modes retrieval cannot fix.

College Students Working Professionals

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Duration: 8 weeks
Level: Intermediate
Format: Live online
Investment: Request pricing
Next cohort: Dates announced soon

Who this is for

Confident Python
Comfortable calling APIs
Basic Git

This is not for

Complete beginners — start with Python for AI Engineering
Anyone looking for prompt tips rather than systems work

What you leave with

A RAG system over a messy real document set
A retrieval evaluation suite separating retrieval quality from answer quality
A cost and latency report with the trade-offs you chose

Tools and stack

PythonAnthropic and OpenAI APIsPostgreSQL with pgvectorsentence-transformersFastAPIRagas

Curriculum

Module 1 — Prompting for reliability

Get structured output every time
Handle refusals and truncation as expected states
Measure variance across runs

Module 2 — Chunking and ingestion

Chunk documents so retrieval works
Handle tables, PDFs and layout
Keep provenance for citation

Module 3 — Embeddings and vector search

Choose and evaluate an embedding model
Build a pgvector index
Understand what similarity is missing

Module 4 — Hybrid search and reranking

Combine keyword and vector retrieval
Rerank for precision
Measure the gain honestly

Module 5 — Answer generation and citation

Ground answers in retrieved context
Cite sources reliably
Refuse when context is insufficient

Module 6 — Evaluating RAG

Separate retrieval metrics from generation metrics
Build a golden set from real questions
Run evaluation in CI

Module 7 — Cost, latency and caching

Profile where time and money go
Cache correctly
Route between models

Module 8 — Where RAG breaks

Recognise the three failure modes retrieval cannot fix
Decide when to use an agent instead
Present your trade-offs

How this program handles evaluation and governance

A confident wrong answer is the characteristic failure of retrieval systems, and it is invisible without measurement. Week 6 makes evaluation a build requirement: retrieval quality measured separately from answer quality, a golden set drawn from real questions, and citation you can check. Week 8 covers refusing to answer, which is often the correct behaviour and almost never the default.