Prompt coverage matrix
A prompt coverage matrix is a fast way to audit whether a prompt actually addresses the situations it will face in production. Most prompt failures are not bad phrasing — they are silent gaps where an edge case (empty input, a hostile question, a missing field) was never mentioned, so the model improvises. This tool cross-references your prompt against a list of edge cases and flags which ones are explicitly handled, only implicitly covered, or completely unaddressed.
How it works
You paste two things: the prompt you want to audit, and a list of edge cases (one per line). The tool breaks each edge case into meaningful keywords, strips common stop-words, and checks how many of those keywords appear in the prompt. A high overlap is scored as handled, a partial overlap as partial (implicitly covered), and no overlap as a gap. It then reports an overall coverage percentage so you can track improvement as you tighten the prompt.
This is intentionally a local heuristic, not a model call. Keyword matching is transparent and instant, and it keeps confidential prompts on your machine. The trade-off is that it measures whether you mentioned a case, not whether your instruction for that case is correct.
Building a useful edge-case list
The quality of the coverage matrix depends almost entirely on the quality of your edge-case list. Generic lists miss the surprising inputs that appear in production. A useful method is to think in three layers:
Universal edge cases — apply to almost any prompt and should always be in the list:
- Empty or whitespace-only input
- Input that is much longer than expected
- Off-topic or completely unrelated input
- Adversarial input designed to override your instructions
- Input in a language other than the one you intended
- Ambiguous input that could reasonably be interpreted two ways
Task-specific edge cases — depend on what your prompt does. For a customer-support reply prompt, relevant cases include “refund request,” “abusive message,” “question outside product scope,” and “multi-part complaint.” For a code-generation prompt, “empty function body,” “syntax that won’t compile,” and “request for unsafe operation” are worth including.
Failure-mode cases — things that have gone wrong before (or that you can imagine going wrong): the model hallucinating a policy it wasn’t given, giving a confident answer when it should abstain, or producing output in the wrong format.
Interpreting the three status values
- Handled means the prompt contains clear keywords that match the edge case — a good sign, though you should still verify the actual instruction is correct and not just present.
- Partial is the most actionable status: something related appears in the prompt, but the case is not addressed by name. This often means the model is expected to infer the right behavior, which is fragile.
- Gap means the case was never mentioned. The model will improvise — which sometimes works, but guarantees nothing.
Tips and examples
Seed your edge-case list with the universals — empty input, oversized input, adversarial or off-topic input, ambiguous wording, missing required data, non-English text, and conflicting instructions — then add cases specific to your task. After running the matrix, rewrite any gap rows into explicit instructions (“If the input is empty, ask the user for X”) and re-run until coverage is high and the partials become explicit. Treat the score as a checklist nudge, then validate the rewritten prompt against real inputs.