Task decomposition prompt builder
Large language models get less reliable the more you cram into one prompt. A single request that asks the model to research, analyse, draft, and format all at once produces muddled output and compounding errors. Decomposition fixes this: split the task into focused steps, run them in sequence, and feed each result into the next. This builder generates that chain for you from a plain description of the goal.
How it works
You describe the task and pick how many steps to split it into. The builder produces a numbered chain where every step states the overall goal, its own narrow job, and a clearly marked handoff slot for the previous step’s output. You choose the handoff style — full, which carries the entire prior result forward, or summary, which asks you to condense it first to keep long chains cheap. Running the steps in order gives each model call a clean, single-purpose context instead of one overloaded prompt.
Tips and notes
- Keep each step to one verb. “Extract”, “analyse”, “draft”, “format” — one job per step is where the accuracy gain comes from.
- Review between steps while designing. Catching an error at step two beats discovering it baked into the final output.
- Use summary handoff for long chains so token cost does not balloon as context accumulates.
- Stabilise, then automate. Once the chain reliably produces good output, the same prompts drop straight into code with programmatic handoffs.