Guide

What this linter actually checks

A system prompt usually gets written once and then reused for every single conversation a deployed assistant has — which means small structural weaknesses in it don't just cause one bad response, they cause a pattern of subtly inconsistent behavior across every interaction. This tool runs a handful of transparent, rule-based checks against your draft — the same categories of issues that show up repeatedly in production system prompts — and flags them explicitly rather than trying to silently "fix" your instructions for you, since only you know what behavior you actually intend.

Why hedging language weakens a system prompt

Phrases like "try to be concise," "if possible, avoid jargon," or "when appropriate, ask a clarifying question" feel polite and reasonable to a human reader, but they hand the model an explicit escape hatch from every one of those instructions. A model reading "try to be concise" has been given implicit permission to not be concise if it decides circumstances warrant otherwise — which, for a system whose whole job is generating plausible-sounding justifications, is a permission it will use more often than you'd like. Replacing hedged language with direct statements ("Keep responses under 3 sentences unless the user asks for more detail") removes that ambiguity entirely.

Why a role statement helps

Opening a system prompt with an explicit role ("You are a technical support assistant for a software product used by non-technical small business owners") does more work than it might seem to at first glance — it implicitly sets vocabulary level, likely areas of expertise, and tone, often more effectively than a separate paragraph of style instructions would on its own. It's not a strictly required ingredient (some well-functioning system prompts skip it entirely in favor of pure behavioral rules), but its absence is worth a deliberate decision rather than an accidental omission, which is why the linter flags it as informational rather than a hard warning.

Why output format needs to be explicit

Without explicit format guidance, a model defaults to its own judgment about response length and structure, which can vary noticeably between similar requests and creates an inconsistent experience for end users interacting with a deployed assistant. Stating the expected format directly — plain paragraphs vs. bullet points, a maximum length, whether to use markdown, whether to always end with a specific type of closing line — removes a layer of guesswork and is one of the highest-leverage additions to most system prompts that lack it.

Spotting rule overload and contradictions

It's genuinely difficult for any automated linter to reliably detect a true logical contradiction between two rules written in natural language — that kind of judgment call is still best done by a careful human read-through. What this tool can do is flag a proxy signal: a high density of absolute language ("always," "never") across the prompt, which correlates with a higher chance that some pair of rules overlaps or conflicts somewhere, especially as a system prompt grows and gets edited by multiple people over time. Treat the warning as a prompt to do that manual review, not as a claim that a specific contradiction has been found.

Structuring a long system prompt

Once a system prompt passes roughly 150-200 words, plain unbroken paragraphs become noticeably harder to scan and maintain — both for you, editing it later, and arguably for the model processing it as input. Breaking a long prompt into clearly labeled sections (Role, Tone, Rules, Format, Examples) with short headers and, where appropriate, numbered or bulleted lists for parallel rules, tends to produce more consistent behavior than the equivalent content as one dense paragraph, and makes future edits far easier to reason about.

Treat your system prompt as a living document

The most effective system prompts in production usage are rarely written once and left alone — they're revised repeatedly based on observed failure cases: a response that came out too long, a tone that drifted, an edge case the original rules didn't anticipate. Keep a version history if you can, and re-run a prompt through this linter after any significant edit, since new hedging language or an unintentionally long paragraph tends to creep back in during routine revisions just as easily as it did in the first draft.

Frequently asked questions