Ground the Model — RAG Engineering Lab

09:00 – 10:15

Decide whether RAG is even the answer

—when to use RAG and when not to, the failure modes, the alternatives

—Takeaway Three real problems triaged — one to RAG, one to fine-tuning, one to a database query.

10:30 – 11:45

Ingest documents the way they actually arrive

—parsing and cleaning, layout-aware extraction, messy PDFs

—Takeaway A realistic document collection parsed clean — including the table that breaks naive extraction.

12:00 – 13:00

Chunk with intent

—chunking strategies, semantic splitting, overlap and metadata

—Takeaway The same corpus chunked three ways with retrieval quality compared — strategy chosen on evidence.

14:00 – 15:15

Embed and search by meaning

—embeddings, vector search, similarity limits

—Takeaway Semantic search running over your corpus — and one query where it loses to keyword search, explained.

15:30 – 17:00

Measure whether the right evidence comes back

—retrieval evaluation, golden questions, recall vs precision

—Takeaway An evaluation set that scores retrieval before any answer is generated — the day-two yardstick.

09:00 – 10:15

Generate answers that cite their evidence

—grounded generation, citations, source attribution

—Takeaway Answers with citations a reader can follow — and a refusal when the evidence is not there.

10:30 – 11:45

Add hybrid retrieval and reranking

—hybrid search, filtering, rerankers

—Takeaway A reranker added and measured — kept only because the evaluation set says it earns its cost.

12:00 – 13:00

Handle the failure cases

—no evidence found, conflicting sources, stale documents

—Takeaway The assistant behaving honestly on the three failure cases users actually hit.

14:00 – 15:15

Evaluate the whole pipeline

—answer-quality evaluation, groundedness, regression checks

—Takeaway End-to-end quality measured and a regression caught on purpose — quality as a number, not a feeling.

15:30 – 17:00

Improve it under time pressure

—diagnosing weak answers, targeted fixes, the write-up

—Takeaway The weakest answers diagnosed to their cause and fixed — with before-and-after scores you keep.