llm
Applied AI 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 do not read characters or words; they read tokens, subword chunks produced by an algorithm like BPE that maps text to integer IDs. Tokenization decides how many tokens a piece of text costs (driving price, latency, and context usage), why models miscount letters or fumble rare words, and why non-English text is more expensive. Applied-AI 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 maximum number of tokens a model can attend to at once, prompt plus generation. It is bounded by attention's quadratic cost, the KV cache's linear memory growth, and the length the model was trained on. A bigger window is not free or uniformly useful (models lose information in the middle), which is why retrieval often beats stuffing everything into context. Applied-AI interviews probe it because it shapes 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 separated instructions and untrusted data, not ad-hoc strings. Applied-AI 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 happens 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. Applied-AI 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
Agents and Tool UseAn agent is an LLM in a loop that can take actions through tools: it reasons, calls a tool (search, a database, code, an API), observes the result, and repeats until done. Tool calling works because the model emits a structured request that your code executes, the model never runs anything itself. The power is doing real work; the cost is reliability and the safety surface (an agent that can act can act wrongly). Applied-AI interviews probe it because agents are where LLMs meet real systems.🤖 Retrieval & Agents
Advanced
Training Reasoning Models: RLVR, PRM vs ORMReasoning models like o1 and R1 are not just 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. Applied AI interviews probe it to see if you understand where the reasoning ability actually comes from.🧠 Foundations of LLMs & GenAI🔒 Premium