90% aren’t prepared for an AI Engineer interview. It’s not just “prompting” or “use LangChain.” Here's a list of questions + concepts you need to know👇
🔹 𝗟𝗟𝗠 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 → What is tokenization, and how does it affect generation? → How do embeddings really work? → What’s the role of attention, positional encoding? → What changes during fine-tuning? (optimizers, schedulers, layer freezing) → LoRA vs QLoRA vs full fine-tune - tradeoffs?
🔹 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 & 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 → Few-shot vs zero-shot - which works better where? → How do you design system prompts that are robust across users? → How do you make output deterministic? → How do you track, version, and backfill changing context? → How do you build/maintain the memory?
🔹 𝗥𝗔𝗚 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 → What’s your chunking strategy - by length, semantics, or structure? → How do you choose a vector DB (Chroma, Pinecone, OpenSearch…)? → Can you update or backfill embeddings with zero downtime? → How do you evaluate retrieval quality (precision@k, reranking, citation)?
🔹 𝗠𝗟𝗢𝗽𝘀 & 𝗟𝗟𝗠𝗢𝗽𝘀 → Sketch a pipeline: from raw data → model → serving → feedback → How would you monitor performance drift or hallucinations? → How do you log prompts and outputs for debugging and auditing? → CI/CD for LLM workflows - what’s different from ML?
🔹 𝗖𝗼𝘀𝘁 & 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 𝗧𝗿𝗮𝗱𝗲𝗼𝗳𝗳𝘀 → How do you reduce token usage? → When should you quantize a model? → What’s your batching + caching strategy to reduce latency? → When to use hosted APIs vs open-source models?
🔹 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 → How do you make an AI system more deterministic and less brittle? → What fallback do you use if the LLM fails mid-task? → Can you solve this without an LLM or vector DB? → What’s the right database for this task - SQL, NoSQL, or vector?
🔹 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀: 1️⃣ What happens if your embedding model changes - how do you migrate safely? 2️⃣ How would you fine-tune a model on user behavior and deploy it? 3️⃣ How would you make this system cheaper without killing quality? 4️⃣ Can you walk me through a debugging session for incorrect LLM outputs?
What’s the realest AI/ML interview question you’ve encountered? Please do comment below and support others 🫶
Techtter
90% aren’t prepared for an AI Engineer interview.
It’s not just “prompting” or “use LangChain.”
Here's a list of questions + concepts you need to know👇
🔹 𝗟𝗟𝗠 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀
→ What is tokenization, and how does it affect generation?
→ How do embeddings really work?
→ What’s the role of attention, positional encoding?
→ What changes during fine-tuning? (optimizers, schedulers, layer freezing)
→ LoRA vs QLoRA vs full fine-tune - tradeoffs?
🔹 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 & 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴
→ Few-shot vs zero-shot - which works better where?
→ How do you design system prompts that are robust across users?
→ How do you make output deterministic?
→ How do you track, version, and backfill changing context?
→ How do you build/maintain the memory?
🔹 𝗥𝗔𝗚 𝗦𝘆𝘀𝘁𝗲𝗺𝘀
→ What’s your chunking strategy - by length, semantics, or structure?
→ How do you choose a vector DB (Chroma, Pinecone, OpenSearch…)?
→ Can you update or backfill embeddings with zero downtime?
→ How do you evaluate retrieval quality (precision@k, reranking, citation)?
🔹 𝗠𝗟𝗢𝗽𝘀 & 𝗟𝗟𝗠𝗢𝗽𝘀
→ Sketch a pipeline: from raw data → model → serving → feedback
→ How would you monitor performance drift or hallucinations?
→ How do you log prompts and outputs for debugging and auditing?
→ CI/CD for LLM workflows - what’s different from ML?
🔹 𝗖𝗼𝘀𝘁 & 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 𝗧𝗿𝗮𝗱𝗲𝗼𝗳𝗳𝘀
→ How do you reduce token usage?
→ When should you quantize a model?
→ What’s your batching + caching strategy to reduce latency?
→ When to use hosted APIs vs open-source models?
🔹 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴
→ How do you make an AI system more deterministic and less brittle?
→ What fallback do you use if the LLM fails mid-task?
→ Can you solve this without an LLM or vector DB?
→ What’s the right database for this task - SQL, NoSQL, or vector?
🔹 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀:
1️⃣ What happens if your embedding model changes - how do you migrate safely?
2️⃣ How would you fine-tune a model on user behavior and deploy it?
3️⃣ How would you make this system cheaper without killing quality?
4️⃣ Can you walk me through a debugging session for incorrect LLM outputs?
What’s the realest AI/ML interview question you’ve encountered?
Please do comment below and support others 🫶
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