How to improve AI prompts without learning prompt engineering

Executive overview

Most people waste time memorising prompt frameworks or buying courses. AI tools can write and optimise your prompts for you in minutes. Two conversation-based methods cover most situations; two free platform tools from OpenAI and Anthropic handle the rest.

The meta skill is knowing to ask AI to improve your prompts — not memorising how to do it yourself.

Two AI-based methods: simple vs. interview

• Simple method: ask the AI to write the prompt for you, specifying the target model and today's date so it researches current best practices.
• Use simple 90% of the time; switch to the interview only for high-stakes or vague tasks.
• AI-generated prompts are often verbose — cut them down; start small and add complexity iteratively.
• Reverse AI interview: prompt the AI to ask you one question at a time, with each answer shaping the next question.
• The interview runs 20–25 exchanges; end it with a stopping prompt asking the AI to generate the system prompt from the context gathered.
• The interview externalises implicit assumptions and produces a prompt hyper-tailored to your specific need.

Built-in prompt optimisers from OpenAI and Anthropic

• OpenAI's optimiser lives in the platform playground; direct URL access is also available — free with an account.
• Anthropic's optimiser is in the console prompt tool; costs ~$1–2 in API credits and lasts months.
• Both tools are trained on best practices for their own models, so optimise for the model you'll actually use.
• OpenAI output uses hashtag delimiters to separate sections; adds a workflow checklist and a self-correction loop if the first draft fails validation.
• Anthropic output uses XML tags for structure; adds explicit simplicity and clarity rules and a stopping condition so the AI knows when it's done.
• Both tools show rationale comments explaining each change — read them to build intuition without studying prompt theory.

Principles for iterating on prompts

• Always start with the smallest prompt that expresses the core intent.
• Add edge cases and nuance one at a time so you can see what each addition does.
• A prompt that's grown complex over iterations is effective; a complex prompt copied from elsewhere is a black box.