# llmgenai/LLMInterviewQuestions

This repository contains LLM (Large language model) interview question asked in top companies like Google, Nvidia , Meta , Microsoft & fortune 500 companies.

Repository: https://github.com/llmgenai/LLMInterviewQuestions
Canonical: https://ross.abutalabs.com/products/llminterviewquestions
License Family: other
Last push: 2025-02-12T12:23:05+00:00

## Health v2 (maintenance only)
Score: 24/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 6, release rhythm 35, longevity 43
- inputs: {"age_days": 615, "days_push": 567, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1926, forks 401 (observed 2026-08-28T04:05:55.631836+00:00)

## What it is
A curated collection of 100+ large language model interview questions from top tech companies, organized into 15 categories covering RAG, prompt engineering, fine-tuning, and deployment. It serves as a study guide for engineers preparing for LLM-related job interviews.

## Use cases
- prepare for llm engineer interview questions
- study rag and prompt engineering interview topics
- find machine learning interview questions asked at google and meta
- review llm fine-tuning and deployment interview concepts
- practice generative ai interview questions
- learn llm system design interview topics

## When to choose
- you are preparing for an LLM, ML, or AI engineer interview at a tech company
- you want a categorized list of real interview questions covering the LLM ecosystem
- you need a checklist of topics like RAG, RLHF, and vector databases to study

## When to avoid
- you need detailed answers or tutorials - the repo mostly lists questions and upsells a paid course
- you want runnable code, libraries, or tools rather than study material
- you need a formally licensed or maintained software project

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, developer-tools
- domain: large-language-models, tutorials, artificial-intelligence
- platform: -
- tags: interview-preparation, llm, rag, prompt-engineering, study-guide, web-server

## Member repositories
- llmgenai/LLMInterviewQuestions (main) score 24

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:55.631836+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 2026-08-30T03:09:13.777710+00:00, confidence not recorded.
  - readme: https://github.com/llmgenai/LLMInterviewQuestions (fetched 2026-08-28T04:05:55.631836+00:00, sha cfa8804ccabe)
- Data as of 2026-08-30T08:39:29.467469+00:00.