From the practice library
Retrieval-Augmented Generation
Grounding LLM outputs with external knowledge via retrieval pipelines
A question to start with
What are the key components of a RAG pipeline?
AI, ML and GenAI interview prep
A focused prep plan for your next AI/ML interview.
Tell us your role and interview date. Learn the gaps, practice real problems, and test yourself with an AI mock interview.
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See how preparation works0+Interview Questions
0+Core Papers Analyzed
0Hands-on Code Labs
0System Design Labs
How crackAI.dev preps you
A personalized interview coach for AI, ML and GenAI engineers. Three inputs in, a plan and the practice to back it out, focused on what the role actually tests.
01
Add resume, JD and date
Drop in your resume, paste the job description (or a link), and set your interview date. crackAI.dev reads the role, the stack, and what it will test.
02
Get a plan tuned to you
A prep plan built for your target role and timeline: what to learn, what to revise, and what to skip, pacing you toward your date.
03
Run real mock interviews
Mock interviews built from your target role and your own projects, graded with feedback, so practice mirrors the loop you will actually face.
Preparation goes deep and wide
From concept to working system. Take RAG, end to end: from “What is RAG?” to “Why RAG over fine-tuning?” to designing a system to building one in an AI pair-programmed coding lab.
It works the same way across the system design labs these roles depend on, from agents to search and recommenders, at beginner to advanced levels.
Everything the role tests, in one place
The practice surfaces AI, ML and GenAI interviews are built on: design, code, fundamentals, and the literature behind them.
Who's behind it
Built by AI engineers from Microsoft, AMD, IIT Bombay and IIIT Hyderabad, with published AI research.
MicrosoftAMDIIT BombayIIIT Hyderabad
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Built on the literature you'll be asked about
- Attention Is All You Need2017
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding2018
- Language Models are Few-Shot Learners2020
- Scaling Laws for Neural Language Models2020
- Training Language Models to Follow Instructions with Human Feedback2022
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks2020
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models2022
- LoRA: Low-Rank Adaptation of Large Language Models2021