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All posts →60 LLM & AI Agent Interview Questions (With Answers)
Sixty questions from real AI engineer and FDE loops, each with the two-or-three-sentence answer you would actually give before the interviewer decides whether to go deeper — grouped by the eight areas they move through, from fundamentals and tradeoffs to memory, tool design, cost, evaluation, security, and the thirteen curveballs that decide most offers.
LangChain vs LlamaIndex vs LangGraph — Which One, and When
LangChain is an integration layer, LlamaIndex is a data layer, LangGraph is a control layer — comparing them head-to-head is like asking whether requests, SQLAlchemy or Celery is the best Python library. Here is the stack drawn out, a three-question decision tree, a side-by-side on the nine dimensions that decide, three worked scenarios including one that needs no framework at all, and the production shape where two of them combine.
RAG vs Fine-Tuning — A Decision Framework (and When to Do Neither)
RAG and fine-tuning are not competing implementations of one idea — they fix different defects. Retrieval changes what is in the context window; fine-tuning changes what the model does with it. Here is the ten-second test that tells them apart, a side-by-side on the eleven dimensions that decide, four worked scenarios, the cost shape nobody models up front, and the four-arm eval that ends the argument in an afternoon.