# amitshekhariitbhu/ai-engineering-interview-questions

Your Cheat Sheet for AI Engineering Interview – Questions and Answers.

Repository: https://github.com/amitshekhariitbhu/ai-engineering-interview-questions
Canonical: https://ross.abutalabs.com/products/ai-engineering-interview-questions
Homepage: https://outcomeschool.com/program/ai-and-machine-learning
Language: Markdown
License: Apache-2.0
License Family: permissive
Topics: agents, ai, ai-agents, ai-engineering, interview, interview-preparation, interview-questions, questions-and-answers, fine-tuning, llm, mcp, quantization, rag
Last push: 2026-08-24T02:57:30+00:00

## Health v2 (maintenance only)
Score: 59/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 35, longevity 11
- inputs: {"age_days": 165, "days_push": 9, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, young
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2867, forks 513 (observed 2026-08-28T04:07:26.552128+00:00)

## What it is
A curated cheat sheet of AI engineering interview questions and answers covering LLM fundamentals, RAG, agents, fine-tuning, and LLMOps. It is a Markdown-based study resource maintained by the founder of Outcome School.

## Use cases
- prepare for an AI engineer interview
- study LLM and RAG interview questions
- review agentic AI concepts before an interview
- find common Gen AI engineer interview questions
- brush up on fine-tuning and quantization topics
- prepare for MLOps or LLMOps role interviews

## When to choose
- you are interviewing for AI, LLM, Gen AI, or MLOps engineering roles
- you want a quick curated question-and-answer reference for AI engineering topics
- you prefer a lightweight Markdown study guide

## When to avoid
- you need hands-on code or runnable examples rather than Q&A text
- you want a comprehensive textbook or structured course on AI engineering
- you need deep mathematical derivations of model internals

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, developer-tools
- domain: artificial-intelligence, large-language-models, tutorials, education
- platform: cross-platform
- tags: interview-preparation, cheat-sheet, llm, rag, ai-agents, fine-tuning, quantization, mcp, questions-and-answers

## Member repositories
- amitshekhariitbhu/ai-engineering-interview-questions (main) score 59

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:26.552128+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:36:05.037702+00:00, confidence not recorded.
  - readme: https://github.com/amitshekhariitbhu/ai-engineering-interview-questions (fetched 2026-08-28T04:07:26.552128+00:00, sha 0d04a1685473)
  - homepage: https://outcomeschool.com/program/ai-and-machine-learning (fetched 2026-08-29T09:51:41.147248+00:00, sha 7cb7eb502986)
- Data as of 2026-08-30T08:39:29.467469+00:00.