What you’re learning
A strong prompt makes the goal, useful context, constraints, and output format clear. Agents can divide independent work, but they need a shared goal and a way to combine and verify their results. A Ralph-style loop repeats a task against explicit completion criteria; it can also repeat mistakes if those criteria are weak.
Work through it
- Use a simple prompt frame. State the goal, provide the relevant context, list constraints, and describe the desired output. Add a success check. Include only details that help the task.
- Improve by iteration. Ask for a first answer, identify what missed the mark, then adjust one part of the prompt. Compare outputs rather than relying on “make it better.”
- Try a two-agent workflow. Give one agent a clear research or drafting subtask and another a review task with a checklist. Have a coordinator combine the results and verify claims against the source material.
- Understand Ralph-style loops. The Ralph Wiggum technique repeatedly feeds a bounded coding task back into an agent until it meets a completion condition or a maximum iteration limit. Use it only for small, reviewable tasks with automated checks and a clear stop condition.
Example command / prompt
Goal: Explain [topic] to [audience]. Context: [relevant facts or source material] Constraints: [must include / must avoid] Output: [format and length] Success check: [how I will verify it]
Keep this in mind. Long-running agent loops can consume time and API usage, and may compound a bad assumption. Set a small iteration limit, use a disposable branch or folder, and review every change before using the result.
Try thisAsk one model for a concise explanation, then give a second model a checklist to review it. Compare the review with your own. Note any unsupported claims.