AI Engineering Interview Questions: A Structured Cheat Sheet, Not a Textbook
Your Cheat Sheet for AI Engineering Interview – Questions and Answers.
At a glance
- What is it?
- Amit Shekhar's repository compiles AI engineering interview questions across 15 topics, linking answers to videos and blog posts. It is a curated index for interview prep, not a self-contained learning resource.
- Who is it for?
- Adopt this repository if you are actively interviewing for AI Engineer, LLM Engineer, or Agentic AI roles and want a curated checklist of questions with linked explanations. It is not for beginners who need in-depth tutorials, nor for those seeking a Q&A text file with full answers.
- Can I use it commercially?
- Yes. Apache-2.0 is a permissive licence: you can use, modify and sell software built on it, as long as you keep its copyright and licence notices.
- Is it still maintained?
- Yes. The repository last received commits 1 day ago.
- What is it written in?
- Mainly Markdown, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 15, 2026, and from our analysis. They are not legal advice.
DEEP OPEN-SOURCE ANALYSIS
What This Repository Actually Provides
The repository is a Markdown file that lists interview questions for roles like AI Engineer, Gen AI Engineer, LLM Engineer, and Agentic AI Engineer. It is organized into 15 sections, from LLM Fundamentals to Behavioral Questions. Each question is followed by a link to an answer, usually on Outcome School's blog, YouTube channel, or a social media post. The README does not contain the answers themselves. It is a pointer index. The author, Amit Shekhar, is the founder of Outcome School, and the repository promotes his paid AI and Machine Learning program. That commercial tie is visible but not hidden. The value proposition is that a candidate can quickly scan topics and identify gaps in their knowledge. The repository is a cheat sheet in the sense of a checklist, not a set of model answers.
Topic Coverage and Depth: Wide but Uneven
The table of contents lists 15 sections, which is broad. LLM Fundamentals alone includes over 30 questions, covering Transformer architecture, tokenization, attention mechanisms, KV cache, and context windows. RAG, AI Agents, Fine-Tuning, and LLMOps each have their own sections. The depth of the linked answers varies. Some links point to detailed blog posts, like 'Decoding Transformer Architecture' or 'Math behind Attention - Q, K, and V'. Others point to social media posts or short video clips. For example, the answer to 'What is temperature?' is a link to an X post. That is not a full explanation. A candidate who wants to understand temperature deeply would need to click through and might find only a one-liner. The repository does not indicate the length or quality of each linked answer. So the coverage is wide, but the depth is inconsistent. You cannot predict whether a link leads to a 10-minute video or a two-sentence tweet.
How the Answers Are Delivered: External Links and the Outcome School Ecosystem
Every answer in the visible portion of the README is a hyperlink to an external resource. The pattern is consistent: a question heading, then a line like 'Answer: Explained in this video:' followed by a URL. Many links point to outcomeschool.com, outcomeschool.substack.com, or the Outcome School YouTube channel. Some point to Amit Shekhar's personal X or LinkedIn posts. This means the repository is not self-contained. It relies on the continued availability of those external pages. If Outcome School ever removes a blog post or a video goes private, the corresponding answer disappears. The repository has no releases, and there is no versioning. The last push was in August 2026, according to the metadata, but that date could be from any future time. There is no changelog. The README says 'We will keep updating this with new questions and answers,' but there is no mechanism to track what changed.
Getting Started: No Installation, Just Browsing
There is no code to install. The repository is a single Markdown file, so you can read it directly on GitHub. To use it, you clone the repository or open the README in a browser. The table of contents has anchor links that jump to each section. For example, clicking 'LLM Fundamentals' takes you to that part of the file. There are no commands to run, no dependencies, and no configuration. This is a documentation project, not a software library. The primary language is Markdown, and the license is Apache-2.0. That license means you can reuse the content, with attribution, even for commercial purposes. However, the linked answers are not covered by that license; they belong to their respective platforms. If you plan to redistribute the question list, be aware that the questions themselves might be original, but the answers are external.
The Real Limitation: No Answers in the Repository
The biggest limitation is that the repository does not contain the answers. It only links to them. If you are offline or if a link breaks, you have a question with no answer. The README says 'Your Cheat Sheet For AI Engineering Interviews,' but a cheat sheet usually implies the answer is right there. Instead, you get a bibliography. For a candidate preparing for an interview, this means you cannot do a quick review in a single document. You must click through dozens of links, which is time-consuming. Also, the questions themselves are not always complete. Some questions, like 'Explain WordPiece and SentencePiece,' have no link at all in the visible portion. That leaves the candidate to research on their own. The repository is honest about its role as a starting point, but it does not deliver the full learning experience. It is a map, not the territory.
Alternatives: Textbooks, Official Docs, and Structured Courses
If you need actual answers in a self-contained format, a textbook like 'Speech and Language Processing' by Jurafsky and Martin covers Transformer architecture, tokenization, and attention in depth. For RAG and agents, the official documentation of frameworks like LangChain or LlamaIndex provides tutorials and API references. Those are real alternatives with a different approach: they give you the material directly, not a list of links. Another alternative is to use the repository as a study guide and then search for each topic on your own. That is essentially what the repository encourages, but it saves you the effort of coming up with the questions. The trade-off is that the repository's linked answers come from one author's perspective, which may not align with what a specific interviewer expects. A textbook or official docs offer broader consensus. For interview-specific practice, platforms like Interview Query or Exponent provide question banks with community-vetted answers. Those are paid services, but they include the answers inline.
Maintenance and License: What to Expect
The repository is actively maintained, with a last push in August 2026. However, there are no releases, so you cannot track stable versions. The content evolves as new questions are added, but there is no changelog to see what changed. The license is Apache-2.0, which is permissive. You can fork the repository, modify it, and use it in your own materials, as long as you preserve the license notice and attribute the original. The linked answers are not covered by that license, so you cannot redistribute them without separate permission. The repository is a single point of failure: if Amit Shekhar stops updating it, the question list will become stale as the field evolves. For example, new topics like MCP (Model Context Protocol) are already included, but future developments might not be. You should check the repository periodically for updates, but there is no notification mechanism except watching the repo on GitHub.
Who Should Use It and How to Verify It First
This repository is best for a candidate who already has a basic understanding of AI concepts and wants a structured revision checklist. If you are a beginner, you will need to supplement it with full tutorials. Before you rely on it, click through a sample of the links to ensure they still work and that the answers are at the depth you need. Check the date of the last push and see if recent developments, like new model architectures or agent frameworks, are covered. The repository includes topics like MCP and quantization, which are current, but you should confirm that the linked answers reflect the latest best practices. Also, consider whether you prefer video explanations or text. Many links are to YouTube videos, which may not be convenient in a quiet study environment. If you need text-only answers, this repository will frustrate you. But if you are comfortable with a mix of videos and blog posts, it is a useful index. The real test is whether the questions match the interviews you face. Look at the list and see if it covers the specific technologies mentioned in the job description. If it does, use it. If not, you will need to find another resource.
Editorial conclusion
Adopt this repository if you are actively interviewing for AI Engineer, LLM Engineer, or Agentic AI roles and want a curated checklist of questions with linked explanations. It is not for beginners who need in-depth tutorials, nor for those seeking a Q&A text file with full answers. Before relying on it, verify that the linked videos and blog posts are still accessible and current, since the repository itself contains only questions and pointers. The value is in the organization and breadth, not in the depth of the content. Use it as a revision checklist, and supplement with hands-on practice and primary documentation for the topics you find weak.
Community notes