Prompt Engineering

Prompt engineering is the practice of designing inputs to LLMs to reliably elicit desired outputs — without changing any model weights. It is one of the highest-leverage skills in AI engineering: the right prompt can turn a mediocre result into a production-ready one.

Ask the model to perform a task with no examples. Works well for tasks the model has seen during training. Prompt: 'Classify the sentiment: "This product is amazing!" Answer with POSITIVE or NEGATIVE.' Best practice: be explicit about the output format. Don't assume the model will guess.

Provide 2–5 examples of input → output pairs before the real query. Dramatically improves accuracy on structured or domain-specific tasks. Key rules: • Keep examples consistent in format • Cover edge cases in your examples • More examples help — but too many waste context • Examples should match the distribution of real inputs

Instruct the model to reason step-by-step before giving a final answer. This dramatically improves performance on math, logic, planning, and multi-step problems. Two approaches: • Zero-shot CoT: append 'Let's think step by step.' • Few-shot CoT: provide worked examples with reasoning shown

Force the model to output in a specific format (JSON, XML, Markdown table) for downstream parsing.