prompting

The most frequently asked applied GenAI question, and the one most candidates turn into a definition dump. What the interviewer wants is a decision framework with a default and the conditions that override it. Here is the call that earns points.

In-context learning is the property that turned prompting into a paradigm; chain-of-thought is its most useful trick. What matters is knowing what 'learning' means here (no weight updates) and why making the model reason step by step actually helps. Here is the answer.

Two prompting strategies that purchase accuracy with extra compute. What matters is knowing how each explores the reasoning space, that they trade tokens and latency for reliability, and how to judge when the problem actually warrants it. Here is that answer.

ReAct is the pattern behind most tool-using agents. What matters is why interleaving reasoning with actions (and observing results) beats reasoning alone or acting alone. Here is the answer that ties the prompt pattern to real agent behavior.

What separates a demo prompt from a production prompt is structure, versioning, and defense. What matters is treating the system prompt as a contract and templates as parameterized, testable artifacts. Here is the answer.

The senior signal is fitting the method to what is genuinely missing rather than reaching for your favorite. Knowledge, behavior, or actions each steer you to a different tool. Here is the decision framework that scores highest.

Token count is latency and dollars, not merely style. 'Be concise' hardly works, and max_tokens just cuts off mid-sentence. Here is how to genuinely shape length without amputating answers.

Few-shot accuracy can swing on example order by itself. If your prompt is fragile to things that shouldn't matter, the fix is structural, not lucky example-hunting. Here is what genuinely reduces variance.

The model's training cutoff is frozen, but your dependencies shift weekly. Retraining is the wrong reflex. Here is how teams keep code suggestions current without touching the weights.

CoT is not a blanket upgrade. On some tasks it does nothing or backfires, and the reason points to what to try instead. Here is the escalation ladder past 'let's think step by step'.

The shift from one model per task to one model adapted many ways rewrote the job description. The interviewer is checking whether you know which half of the work got nearly free, which half did not move at all, and which problems classical ML still owns outright.

Three different systems are failing at once (the tokenizer, the model, and your pipeline) and most candidates blur them into one. Separating them tells you which fix buys the most, and the highest-leverage one is not the prompt.

The most repeated trick in prompt engineering, and the evidence for it is much thinner than the folklore. A screening question for whether you measure your prompts or inherit them from a thread of screenshots.

Context engineering is the discipline that took over from 'prompt engineering' for serious agent work. The signal is grasping that what you place in the context window (and what you omit) shapes behavior more than clever wording. Here is the answer.