prompting

The single most-asked applied GenAI question, and the one most candidates answer as a definition dump. The interviewer wants a decision framework with a default and the conditions that flip it. Here is the call that scores.

In-context learning is the property that made prompting a paradigm; chain-of-thought is its most useful trick. The signal 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 buy accuracy with extra compute. The signal is knowing how each explores the reasoning space, that they trade tokens and latency for reliability, and how to decide when the problem actually warrants it. Here is that answer.

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

The difference between a demo prompt and a production prompt is structure, versioning, and defense. The signal is treating the system prompt as a contract and templates as parameterized, testable artifacts. Here is the answer.

The senior signal is matching the method to what is actually missing instead of defaulting to your favorite. Knowledge, behavior, or actions each point at a different tool. Here is the decision framework that scores highest.

Token count is latency and dollars, not just style. 'Be concise' barely works, and max_tokens just truncates mid-sentence. Here is how to actually shape length without amputating answers.

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

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

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

Context engineering is the discipline that replaced 'prompt engineering' for serious agent work. The signal is understanding that what you put in the context window (and what you leave out) drives behavior more than clever wording. Here is the answer.