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How to prepare for AI engineer interviews with course material with AI Engineering Course

Use the interview module, glossary, and FAQs to drill core AI engineering terms.

  • Research
  • Beginner
  • 15 minutes
  • From the README
  • Any agent

Who it is forA candidate with aScheduled AI engineer, LLM engineer, or ML engineer interview who needs a fast review of key concepts.

The problemInterview questions cover many terms across training, RAG, agents, and inference, and it is hard to review them all.

The outcomeA short list of concepts to explain from memory, each linked to the lesson that covers it.

What you need first

  • A web browser to open the GitHub repo and read the lessons
  • Basic programming knowledge, preferably Python, for the code examples
  • Time to read lessons in order, module by module

Steps

  1. Open the repo at https://github.com/amitshekhariitbhu/ai-engineering-course and go to Module 18: Prepare for AI Engineering Interviews.
  2. Read the AI Engineering Key Concepts Glossary section.
  3. Read the AI Engineering Course FAQs section.
  4. Pick five terms from the glossary that match the job you are applying for, such as RAG, KV cache, or LoRA.
  5. Explain each term in your own words, then open the matching module to check your explanation.

Prompt for your agent

Read the README of amitshekhariitbhu/ai-engineering-course, take the terms in the Key Concepts Glossary, and create a quiz of 10 questions for <target role>. Output each question with the module it comes from, and verify that every answer points to a section that exists in the README.

Caveat. The README lists interview preparation as a module and a glossary, but the actual question content lives in the linked lessons, which you should open to confirm.

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