Prompting

Chain-of-thought prompting asks the model to show its reasoning step by step before giving a final answer. It dramatically improves performance on multi-step problems.

Few-shot prompting includes a small number of input-output examples directly in the prompt so the model can pattern-match without any fine-tuning.

A prompt is the text you send to an LLM to elicit a response. It typically includes a system message, optional examples, and the user's query.

Prompt engineering is the craft of writing prompts that reliably produce the behavior you want from an LLM. It blends formatting, examples, tone, and constraints.

Self-consistency samples N chain-of-thought completions for the same problem and takes the majority answer. Improves accuracy on math and reasoning tasks at N× the cost.

The system prompt is the first message in a chat that sets the model's persona, rules, and overall behavior. It is treated by most providers as higher-trust than user input.

Test-time compute is the extra reasoning, sampling, or search a model can do at inference time to improve quality — more thinking tokens, more candidate answers, or verifier-guided search.

Tree of Thoughts generalizes chain-of-thought to a search tree: at each step the model produces multiple candidate thoughts, evaluates them, and explores the most promising branches — like beam search over reasoning.

Zero-shot prompting asks the model to perform a task without showing any examples — only the instruction and the input. Modern instruction-tuned models do this well.