10 tools
Prompt Engineering Tools
Write, check and repair the text that drives everything else.
A prompt is the highest-leverage artefact in an LLM application and usually the least reviewed. It accumulates instructions over months, nobody deletes anything, and it is billed on every single request.
These tools treat prompts the way you would treat code: lint them, check the delimiters balance, extract the variables, measure what a change cost.
- AI Prompt GeneratorPick the task, get a structured prompt that already works.
- Prompt LinterNine checks for the mistakes that make prompts expensive and unreliable.
- Prompt Whitespace CleanerRemove the invisible characters you are paying for on every request.
- XML Tag Prompt WrapperDelimit your sections so the model knows where the data stops.
- Prompt Delimiter and Tag CheckerFind the unclosed tag before the model reads your instructions as data.
- Few-Shot Example FormatterTurn a list of pairs into properly formatted examples, four ways.
- Prompt Template FillerFill a template and see the real prompt, including what did not get filled.
- Prompt Template Variable ExtractorList every variable a template needs, with usage counts and a typed skeleton.
- Stop Sequence TesterSee exactly where generation stops, and what you paid for after it.
- System Prompt to Skills SplitterFind the conditional instructions you are paying for on every request.
Which one you need
Run the linter first on any prompt you did not write today. It finds the things that are invisible when you know what you meant: unmeasurable words like "brief" and "appropriate", contradictory instructions, and edge cases nobody decided.
Use the delimiter and XML tools when the prompt mixes your instructions with somebody else’s text. That mixture is where prompt injection lives, and delimiting is the cheapest mitigation there is.
The few-shot and template tools are for the stage after that — once the structure is right and the question becomes whether the examples are doing anything.
The cache checker is worth running on any prompt that goes to production, because a single dynamic value near the top can turn every cached read into a full-price request without anything appearing broken.
Questions
Does prompt structure really matter, or is it superstition?
The structural parts are measurable and the folklore is not. Stating the task first, delimiting data from instructions, showing two examples rather than describing a format, and defining the edge cases all produce repeatable improvements. Collecting phrases that worked once does not.
How long should a prompt be?
Short enough that every line earns its place on every request, forever. There is no word limit worth stating, but there is a discipline: read it line by line each quarter and delete what stopped mattering. Prompts accrete and almost nobody trims them.