Arun Sharma / ReadingThe RAG Interview

An unofficial study edition

The RAG Interview

Retrieval, Ranking, Grounding,
and System Design.

From first principles to production trade-offs. Explore the entire Notion study collection, organized for reading, practice, and reference.

41 chapters12 parts7 appendices

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Unofficial study notes on The RAG Interview by Hao Hoang. No affiliation with or endorsement by the author is claimed, and rights in the source work remain with the author.
Source metadata: Hao Hoang · AI Interview Prep · First edition, 2026.
About this edition and source rights

The curriculum

Build the system.
Understand every trade-off.

Twelve parts, from framing the question to designing systems at scale. Open a part to explore its chapters.

01The RAG Interview LandscapeChapters 1–3
  1. 1: What a RAG Interview Actually Tests
  2. 2: The Competing Answers to the Same Problem
  3. 3: A Repeatable Framework for Any Retrieval-Augmented Generation (RAG) Design Question
02The Generator SideChapters 4–8
  1. 4: What the LLM Brings and What It Breaks
  2. 5: Data, Privacy, and the Legal Surface
  3. 6: Scaling Laws and the Economics of Retrieval vs Parameters
  4. 7: In-Context Learning - The Mechanism RAG Rides On
  5. 8: Reading the Machine: Circuits, Induction Heads, and Attribution
03Prompting and Context ConstructionChapters 9–11
  1. 9: Prompting for Retrieval-Augmented Systems
  2. 10: Prompt Sensitivity
  3. 11: Abstention and Calibration
04Representing What You RetrieveChapters 12–14
  1. 12: Text Representation
  2. 13: Chunking and Granularity
  3. 14: Beyond Plain Text - Tables, Layout, Documents
05Indexing and Vector SearchChapters 15–17
  1. 15: Approximate Nearest Neighbor Search
  2. 16: Compression and Index Economics
  3. 17: Operating a Vector Store
06Retrieval and RankingChapters 18–23
  1. 18: Classical Information Retrieval (IR) You Are Expected to Know
  2. 19: Learning to Rank
  3. 20. Dense Retrieval
  4. 21: Learned Sparse and Multi-Vector Retrieval
  5. 22: Reranking
  6. 23: Generative Retrieval
07Query Understanding and Control FlowChapters 24–26
  1. 24: Query Reformulation
  2. 25: Routing and Adaptive Retrieval
  3. 26: Iterative, Recursive, and Agentic Retrieval
08Training the RAG SystemChapters 27–29
  1. 27: Fine-tuning the Generator
  2. 28: Training the Retriever
  3. 29: Bootstrapping Training Data
09Generation and Context AssemblyChapters 30–31
  1. 30: Lost in the Middle
  2. 31: Attribution and Citation
10EvaluationChapters 32–34
  1. 32: Evaluating Retrieval
  2. 33: Evaluating Generation
  3. 34: Evaluating the System
11Trust, Credibility, and Adversarial RobustnessChapters 35–36
  1. 35: Source Credibility
  2. 36: Provenance and Adversarial Robustness
12Scaling, Advanced Variants, and Design DrillsChapters 37–41
  1. 37: Latency, Cost, and Systems
  2. 38: Distributed and Federated RAG
  3. 39: Multimodal RAG
  4. 40: Graph RAG
  5. 41: End-to-End Design Drills

Reference & practice

Keep the essentials close.

01 / ReferenceFormula Sheet02 / RehearseQuestion Bank03 / ReviewDesign Checklists04 / DecodeNotation and Symbols

Complete contents →Source index →

The book’s starting model

Knowledge in weights.
Evidence in a datastore.

Retrievable knowledge and model parameters play different roles. This original diagram from Chapter 1 introduces the two paths.

Chapter 1: the semi-parametric frame
Chapter 1: the semi-parametric frame · Open full size (new tab)
Diagram description and source
flowchart TD
    Q[query x]
    D[D: non-parametric datastore. Capacity grows with stored rows. Write one embedded chunk and row. Reversible in seconds.]
    W[[θ: parametric weights. Capacity fixed before training. Write with a gradient pass over every parameter. Entangled and measured in GPU-hours.]]
    R[RET returns c_1 through c_k]
    G[GEN reads x and c_1 through c_k]
    Y[answer y]
    Q -->|retrieve| D
    Q -->|condition| W
    D --> R
    R --> G
    W --> G
    G --> Y
    style D stroke-dasharray: 5 5

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Unofficial study notes on The RAG Interview by Hao Hoang. No affiliation with or endorsement by the author is claimed, and rights in the source work remain with the author. Hosted by Arun Sharma.

Source metadata: Hao Hoang · AI Interview Prep · First edition, 2026. Attribution & rights · Source 03175ac

Contents

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