Retrieval-Augmented Generation

Retrieval-augmented generation is the most widely deployed pattern in this field and the one

with the least measurement behind its defaults. Chunk at 500 tokens with 50 of overlap.

Retrieve five passages. Add a reranker. Use hybrid search. Each of those is repeated

everywhere and justified almost nowhere.

The result is a system with several tuning points, no instrumentation, and a single accuracy

number that cannot say which of its two stages is failing. When it works, nobody knows which

choice mattered. When it does not, the fixes are attempted in the order they were heard

rather than the order the evidence supports.

The obstacle to measuring retrieval is knowing what the right answer was — and human

relevance judgements are expensive, subjective and unavailable at the scale a book needs.

This book sidesteps that entirely by building corpora whose correct answers are true by

construction. Queries are drawn as spans from a text, so the chunk containing that span is

correct by definition. Documents describe invented entities, so no retriever can succeed by

prior knowledge, and each fact appears exactly once, so attribution can be checked exactly.

Nothing here is scored against a judgement. Every number is scored against a fact about how

the corpus was built.

Four of the results contradict advice that circulates as settled:

- **Hybrid search made things worse.** Combining a strong lexical retriever with a weaker

dense one lost on every metric in every query condition — and a fusion-weight sweep fell

monotonically with no interior optimum, which turns out to be a reusable diagnostic: a peak

in the middle means fusion is buying something, a monotonic curve means drop the weaker

retriever rather than tune the fusion.

- **Neither reranker beat the retriever** — and, more importantly, none of the differences

exceeded the noise floor, which this book computes before reporting any of them. The same

table, reported without the floor, would support two opposite headlines.

- **Attribution collapses long before retrieval does.** With near-duplicates in the corpus,

the answer was still retrieved 92% of the time while the fraction of claims traceable to a

single source fell from 1.000 to 0.170. A citation feature becomes arbitrary while every

retrieval metric looks healthy.

- **Retrieval scores are a usable abstention signal** — unlike a model's own confidence. On

50 queries about entities absent from the corpus, the highest scored 1.62 against a

threshold at 2.94, with nothing in between.

And two that confirm the advice, with the numbers behind it:

- **Chunk overlap is the cheapest fix in the book.** Ten words of overlap took the rate of

answers cut by a boundary from 10.0% to 0.5%, for a 9% larger index — while chunk size,

the parameter people reach for, costs prompt tokens on every request instead.

- **Retrieval depth trades signal for cost brutally.** Going from one chunk to twenty

multiplied the chance the answer was present by 2.4 and the token cost by 20, ending with

95% of the context irrelevant.

One boundary is stated plainly rather than hidden. A RAG pipeline has two stages and this

book can measure only one directly: retrieval is deterministic and is measured exactly;

generation requires a capable language model the build environment does not have. So

generation is either simulated with a controllable accuracy — which is enough to derive the

arithmetic of a two-stage pipeline exactly — or cited and marked in a visually distinct box.

No claim about what a model does with retrieved context is presented as if measured here.

What you get:

- 8 chapters covering the two-stage pipeline, chunking, lexical and dense and hybrid

retrieval, retrieval depth and reranking, duplication, evaluating two stages separately,

citation and abstention, and a final chapter of limits.

- 42 original practice questions, tagged by certification and by difficulty, where every

option is explained — not just why the right answer is right, but why each wrong answer is

wrong, because on these exams the distractors are where the teaching is.

- A reverse map from each certification to the chapters that serve it, in reading order.

- All the code — every retriever, every experiment — so each table and figure can be

regenerated.

Written to the published objectives of Databricks Certified Generative AI Engineer, NVIDIA

NCP-GENL, NVIDIA NCA-GENL, NVIDIA NCP Agentic AI, and AWS Certified Generative AI Developer –

Professional. Objectives were checked in August 2026; confirm the current blueprint with the

certifying body before you sit.

You need to be able to read Python. Embeddings, lexical scoring, ranking and evaluation are

built here from the ground up, and nothing in this volume assumes you have read any other

book.

Every question is original, written from published exam objectives. Nothing is reproduced

from, or based on recollection of, any live examination.

This book was created through a process that combines careful human planning, content direction, and advanced AI technology, followed by thorough refinement and review to ensure a high-quality final work.