Prompt engineering

The four parts remove guesswork and turn generic, forgettable output into targeted, useful responses.

Specifying who the output is for is the fastest single quality boost you can add.

Showing balanced examples teaches patterns better than paragraphs of rules and keeps the model neutral and on-format.

Specifying columns, keys, and format (list/table/CSV/JSON) yields data you can use instead of walls of text.

Telling the model to use null or N/A for blanks prevents invented values and inconsistent rows.

JSON mode guarantees a consistent shape every time, which manual prompting cannot reliably ensure for repeated tasks.

Explicit reasoning flips risky guesses into checkable chains, and a labeled final answer lets you skim to verify.

Reasoning models do chain of thought automatically, handling complex tasks better without manual prompting tricks.

Small surgical edits are easy to steer and undo, and the model already remembers the chat.

This prevents surprising full rewrites that ruin the parts you already liked.

The model does each task better when focused, and you can verify each handoff before proceeding.