00.09 About the Author

00-START-HERE · 01-READING-ROUTES · 00_08_acknowledgments · 01_what_a_rag_interview_actually_tests This unit preserves the author’s supplied profile, public-writing description, authorship statement, motivation and correction routes. Biographical details and audience figures are attributed to that profile; they are not independently authenticated or live-updated here.

TL;DR

The supplied profile identifies Hao Hoang as an applied AI/ML engineer and technical writer, based in Los Angeles and originally from Quang Tri, Viet Nam.
He states that he owns AI Interview Prep, publishes daily on Substack/LinkedIn and is the sole original author without co-authors or ghostwriters. Preserve this authorship statement without treating it as an independent audit of the original writing process.
The profile reports more than $70{,}000$ LinkedIn followers and $16{,}000$ Substack readers, with no as-of date. These are different channel measures, not a deduplicated total or current live counts.
He describes RAG’s prevalence and fragmented interview material as his motivation; this is personal experience, not an industry-wide frequency estimate.
The listed contact routes and email correction process are preserved. No employer, qualification, current readership or other missing biographical fact is inferred.

The story

Picture a public workshop run by Hao Hoang, an applied AI/ML engineer and technical writer. The workshop is based in Los Angeles, while a plaque records his origin in Quang Tri, Viet Nam. AI Interview Prep is the name above the door. One workbench is Substack, where he lays out Daily AI Interview Questions and longer technical notes. A second bench is LinkedIn, where the same ideas appear as shorter daily posts between editions. The supplied profile reports more than $70{,}000$ LinkedIn followers and $16{,}000$ Substack readers. Their dates, definitions and overlap are not given, so do not sum them into a unique-person audience. The author describes his own sole-authorship process and motivation; these remain attributed statements rather than independently verified biography. The email correction route invites readers to check derivations and assumptions, not merely repeat numbers.

Decoder table

Source term or claim	Plain-language decoding	Why it matters in this unit
Hao Hoang	The named author speaking in the profile.	It establishes who wrote the book.
Applied AI/ML engineer	Applied artificial intelligence and machine learning engineer	It states his professional role without adding an employer or affiliation.
Technical writer	His second stated professional title.	It connects his technical work with his public explanations.
Los Angeles, California	His stated current base.	It preserves the location claim exactly.
Quang Tri, Viet Nam	His stated place of origin.	It preserves the origin claim exactly.
AI Interview Prep	The named entity he says he owns.	It preserves his ownership statement without inferring an entity type.
Writes for the community every day	His stated publishing cadence and audience orientation.	It preserves the daily and community-facing claim.
Substack	The venue for Daily AI Interview Questions and longer notes.	It preserves the named series and longer-note channel.
Daily AI Interview Questions	The named Substack series.	It identifies a recurring publication exactly.
LLM system design	Large language model system design, one longer-note topic	It is one of the technical areas listed by the author.
Reinforcement learning	One longer-note topic named by the author.	It preserves the stated scope of his writing.
Vision-language models	One longer-note topic named by the author.	It preserves another stated topic without adding a definition.
Retrieval-augmented generation	One longer-note topic and the subject of this book.	It ties the public writing to the book.
Other high-signal topics	Additional topics for engineers and researchers preparing for rigorous AI interviews.	It keeps the author’s broader scope without inventing examples.
Engineers and researchers	The people preparing for rigorous AI interviews whom the notes serve.	It identifies the stated audience.
LinkedIn	The venue for shorter daily posts that carry the same thread of ideas.	It lets people follow between Substack editions.
More than seventy thousand LinkedIn followers	Source-reported channel figure without an as-of date	Not a live verified count or a unique-audience estimate
Sixteen thousand Substack readers	Source-reported channel figure without an as-of date	Definition and overlap with LinkedIn are unspecified
Teach, learn in public, and answer	The three roles he assigns to his public writing.	He calls it the main place where he teaches, learns in public, and answers what the community is asking about.
Sole original author	The profile’s statement about the original research/writing	Preserved attribution, not an independent writing-process audit
Without co-authors or ghostwriters	An explicit qualification in the source’s authorship statement	Do not infer verification of undisclosed activity from this page
Deployed most often and understood least well	The author’s personal experience of RAG	Not an industry-wide deployment or comprehension estimate
Competent RAG answer versus a superficial one	The distinction the book’s questions are meant to expose.	It frames the expected depth of interview understanding.
Papers, courses, and production postmortems	The separate places where those questions had been scattered.	It gives the second reason for collecting them in one book.
Email	`hao.hoang.ai@gmail.com`	It is the stated direct contact and correction route.
Website	`https://haohoang.is-a.dev/`	It is the stated website contact.
Substack address	`https://aiinterviewprep.substack.com/`	It is the stated publication address.
LinkedIn address	`https://www.linkedin.com/in/hoang-van-hao`	It is the stated LinkedIn route.
Errata and corrections	Reader reports about mistakes in the book.	The author welcomes them by email to improve the next edition.
Numbers should be derived rather than repeated	The book’s stated argument about quantitative claims.	It makes arithmetic checking part of the book’s own standard.
Shorter, daily posts	The format of his LinkedIn writing.	It preserves both the shorter format and daily cadence.
Follow along between editions	The reason given for sharing the same ideas on LinkedIn.	It states the continuity between Substack editions.
Main place	The role public writing has in his teaching, public learning, and community answers.	It preserves the author’s own emphasis.
Every chapter	The scope of his research and writing claim.	It prevents sole authorship from being weakened to only part of the book.
Fastest way to improve the next edition	The author recommends emailed corrections	No response-time or revision-time service guarantee
Arithmetic check	A reader derives or checks a number and finds a mistake.	The source says this follows the book’s own argument.

Core mechanics

The profile states no monetary cost. It describes daily writing and researching/writing the original chapters as the author’s effort. That description is not a measured labor estimate or an independent audit of authorship.

Component	What it is	Why it exists	What would be missing without it	Source-stated cost or effort
Identity	Hao Hoang describes himself as an applied artificial intelligence and machine learning (AI/ML) engineer and technical writer.	The profile introduces the person behind the book.	The reader would not know the author’s name or stated professional roles.	No explicit cost is stated.
Location and origin	He is based in Los Angeles, California, and originally from Quang Tri, Viet Nam.	The profile supplies his stated geographic context.	The reader would lose both the current-base claim and the origin claim.	No explicit cost is stated.
AI Interview Prep	He says he owns AI Interview Prep.	It preserves the stated ownership claim.	The profile would lose the named entity and its stated owner.	No monetary cost is stated.
Daily public writing	He writes for the community every day on Substack and LinkedIn. He calls public writing the main place where he teaches, learns in public, and answers what the community is asking about.	It preserves the cadence and all three stated roles of the practice.	The profile would lose the cadence and the three stated purposes of the practice.	The stated effort is writing every day.
Substack program	He publishes Daily AI Interview Questions and longer notes on large language model (LLM) system design, reinforcement learning, vision-language models, retrieval-augmented generation, and other high-signal topics. The audience is engineers and researchers preparing for rigorous AI interviews.	It provides the longer-note channel and names its subject range and audience.	The named series, technical topics, longer format, and intended audience would be missing.	No explicit cost is stated.
LinkedIn program	He shares the same thread of ideas in shorter daily posts.	It lets people follow along between Substack editions.	The short-form channel and continuity between editions would be missing.	The stated effort is daily posting.
Public reach	The profile reports more than $70{,}000$ LinkedIn followers and $16{,}000$ Substack readers.	Records two source-reported channel measures.	Do not infer a current count or a deduplicated total.	As-of dates, metric definitions and overlap are not given.
Authorship	The profile names Hao as sole original author without co-authors or ghostwriters.	Preserves the supplied authorship claim.	Do not independently certify the original writing process.	Researching/writing every chapter is stated effort, not measured hours.
Book motivation	He sees retrieval-augmented generation as the pattern deployed most often and understood least well. He also found that the questions separating competent RAG answers from superficial ones were scattered across papers, courses, and production postmortems rather than collected anywhere.	The book collects material that he found fragmented.	The reader would not know why he believed the book was needed.	No explicit cost is stated.
Contact routes	The email is `hao.hoang.ai@gmail.com`. The website is `https://haohoang.is-a.dev/`. The Substack address is `https://aiinterviewprep.substack.com/`. The LinkedIn address is `https://www.linkedin.com/in/hoang-van-hao`.	They provide the four stated ways to reach or follow him.	Readers would lack the source-provided contact information.	No explicit cost is stated.
Correction loop	Errata and corrections are welcome at `hao.hoang.ai@gmail.com`. The author calls email the fastest way to improve the next edition.	Reader checks can surface arithmetic mistakes and improve the next edition.	The book would lack a stated feedback route and a connection between correction and its quantitative standard.	A reader checks the arithmetic and finds a mistake. No monetary cost is stated.

Diagrams

The manifest records zero figures and zero tables for this unit. The page contains no portrait, numbered source visual, or source table. Its unnumbered four-item contact list is reproduced in the decoder and Core mechanics.

Whiteboard pack

Numbered drawing order

Write Hao Hoang in the center.
Add Applied AI/ML engineer and Technical writer beneath his name.
Add Based in Los Angeles, California and Originally from Quang Tri, Viet Nam on the left.
Draw a branch to AI Interview Prep on the right.
Split that branch into Substack and LinkedIn.
Under Substack, write Daily AI Interview Questions, then list the longer-note topics.
Under LinkedIn, write Shorter daily posts and Follow between editions.
Add More than 70,000 followers and 16,000 readers beside the two channels.
Draw a book below the center and label it Sole author, Every chapter, and No co-authors or ghostwriters.
Finish with arrows from Papers, Courses, and Production postmortems into the book, then draw Email corrections as a feedback arrow into the next edition.

90 to 100 word script

Start with Hao Hoang at the center. Label him an applied AI/ML engineer and technical writer. Add Los Angeles, California, as his base and Quang Tri, Viet Nam, as his origin. Branch to AI Interview Prep, which he owns, then split into Substack and LinkedIn. Put Daily AI Interview Questions and the longer-note topics under Substack. Put shorter daily posts under LinkedIn. Add more than $70{,}000$ followers and $16{,}000$ readers. Finish with sole authorship, the scattered questions that motivated the book, all four contact routes, and email corrections feeding the next edition.

Interview traps

1. Who wrote the book?

The profile names Hao Hoang as the sole original author and states that he researched and wrote every chapter without co-authors or ghostwriters. This review preserves that attributed statement; it does not independently verify the original writing process or reassign authorship.

2. What is the difference between his Substack and LinkedIn writing?

Substack carries Daily AI Interview Questions and longer technical notes. LinkedIn carries the same thread of ideas in shorter daily posts so people can follow between editions.

3. Why did he write this book?

He says retrieval-augmented generation is the pattern he sees deployed most often and understood least well. He also says the questions that separate a competent RAG answer from a superficial one were scattered across papers, courses, and production postmortems rather than collected anywhere.

4. What audience scale does the page state?

The profile reports more than $70{,}000$ LinkedIn followers and $16{,}000$ Substack readers, without an as-of date. Different metric definitions and possible cross-channel overlap prevent a deduplicated audience total; these are not independently verified live counts.

5. How should a reader report a correction?

The page welcomes errata and corrections at hao.hoang.ai@gmail.com. It calls email the fastest way to improve the next edition and links arithmetic checking to the book’s rule that numbers should be derived rather than repeated.

Key numbers

Measure	Source-stated value
Author	1 sole author
Publishing cadence	Every day
Public writing channels	2, Substack and LinkedIn
Named longer-note topics	4, plus other high-signal topics
LinkedIn followers	Source-reported $\gt 70{,}000$ ; no as-of date
Substack readers	Source-reported $16{,}000$ ; no as-of date
Public-writing roles	3, teach, learn in public, and answer community questions
Places where the questions were scattered	3, papers, courses, and production postmortems
Contact routes	4, email, website, Substack, and LinkedIn

00-START-HERE · 01-READING-ROUTES · 00_08_acknowledgments · 01_what_a_rag_interview_actually_tests