reasoning

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.

Chain-of-thought looks like the model showing its work, but it may not mirror the real computation. The signal is knowing CoT can be a post-hoc story, with evidence, and what that breaks for safety and oversight.

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'.

Single-shot retrieval breaks on questions that chain facts ('who directed the highest-grossing film of 2019?'). The signal is decomposing or iterating retrieval rather than retrieving once, and guarding against errors compounding across hops. Here is the answer.