06-22-2026, 01:27 PM
Prompt engineering gets a lot of hype and a lot of snake oil. Separating what actually works from "imagine you are a senior expert with 30 years experience" nonsense is worth doing.
Things that genuinely help in my experience:
Chain of thought. Asking the model to reason step by step before giving an answer measurably improves accuracy on anything multi-step. "Think through this step by step" or "show your reasoning" both work.
Role assignment when it makes sense. "You are a code reviewer focused on security" works better than "review this code for security issues" because it sets context before the task, not alongside it.
Few-shot examples. If you want output in a specific format, show it. One or two examples of input/output pairs remove ambiguity completely.
Constrain the output. "Give me 3 options, not more" or "respond in under 100 words" reduces rambling and forces prioritisation.
Iterate on what fails. If the response is wrong, don't just re-send. Add a line explaining why it was wrong. The model can self-correct with context.
Avoid being vague about the goal. "Make this better" is useless. "Make this more concise while keeping all the technical detail" is specific.
What prompting techniques have you found actually move the needle?
Things that genuinely help in my experience:
Chain of thought. Asking the model to reason step by step before giving an answer measurably improves accuracy on anything multi-step. "Think through this step by step" or "show your reasoning" both work.
Role assignment when it makes sense. "You are a code reviewer focused on security" works better than "review this code for security issues" because it sets context before the task, not alongside it.
Few-shot examples. If you want output in a specific format, show it. One or two examples of input/output pairs remove ambiguity completely.
Constrain the output. "Give me 3 options, not more" or "respond in under 100 words" reduces rambling and forces prioritisation.
Iterate on what fails. If the response is wrong, don't just re-send. Add a line explaining why it was wrong. The model can self-correct with context.
Avoid being vague about the goal. "Make this better" is useless. "Make this more concise while keeping all the technical detail" is specific.
What prompting techniques have you found actually move the needle?