llm
AI, ML & GenAI interview questions tagged llm, across every topic.
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Concepts behind "llm"
The curriculum that explains the ideas these questions test.
Foundational
TokenizationModels read neither characters nor words; they read tokens, subword chunks produced by an algorithm like BPE that maps text to integer IDs. Tokenization sets how many tokens a piece of text costs (driving price, latency, and context usage), why models miscount letters or stumble on rare words, and why non-English text costs more. AI, ML, and GenAI engineer interviews probe it because token accounting is the first thing that bites a production LLM bill.🧠 Foundations of LLMs & GenAI
Foundational
The Context WindowThe context window is the largest number of tokens a model can attend to at once, prompt plus generation. It is capped by attention's quadratic cost, the KV cache's linear memory growth, and the length the model trained on. A bigger window is neither free nor uniformly useful (models lose information in the middle), which is why retrieval often beats cramming everything into context. AI, ML, and GenAI engineer interviews probe it because it drives cost, latency, and the RAG-vs-long-context decision.🧠 Foundations of LLMs & GenAI
Foundational
Prompt EngineeringPrompting is the cheapest, fastest way to steer an LLM: clear instructions, few-shot examples, explicit output format, and the right context. It is the first technique to try before reaching for RAG or fine-tuning, and in production it means versioned, tested prompt templates with instructions kept separate from untrusted data, not ad-hoc strings. AI, ML, and GenAI engineer interviews probe it because most LLM features ship on prompting alone, and because sloppy prompts are a top source of unreliability and injection risk.🧠 Foundations of LLMs & GenAI
Foundational
HallucinationA hallucination is fluent, confident output that is wrong or unsupported. It arises because a language model is trained to produce plausible continuations, not to know what it knows; it has no built-in truth check. You reduce it with grounding (RAG), letting the model abstain, low temperature on factual tasks, and verification, and you detect it with faithfulness checks against sources. AI, ML, and GenAI engineer interviews probe it because hallucination is the number-one reason LLM features fail in production, and because the fix is system design, not a magic prompt.🧠 Foundations of LLMs & GenAI
Foundational
What an LLM Is: Next-Token Prediction and the Training PipelineAn LLM is a function that maps a sequence of tokens to a probability distribution over the next token, called in a loop. Three stages turn that function into an assistant: pretraining on a huge corpus buys knowledge and fluency, supervised fine-tuning teaches it to answer rather than continue, and preference alignment teaches it which answer a human prefers. AI, ML, and GenAI engineer interviews probe this because capability comes from pretraining while behavior comes from post-training, and almost every production complaint is a behavior complaint.🧠 Foundations of LLMs & GenAI
Advanced
Training Reasoning Models: RLVR, PRM vs ORMReasoning models like o1 and R1 are more than bigger instruct models: they are trained with reinforcement learning where the reward comes from checking whether the final answer is correct, which teaches the model to generate long internal chains of thought. This page covers RL with verifiable rewards (and GRPO specifically), the split between process reward models that score each step and outcome reward models that score only the answer, and how that choice shapes test-time search. AI, ML, and GenAI interviews probe it to see whether you understand where the reasoning ability actually comes from.🧠 Foundations of LLMs & GenAI🔒 Premium