Prompts
Claude reads structure differently than ChatGPT — here's how to write for that
A prompt copy-pasted from a generic ChatGPT list often works less well in Claude, not because Claude is worse, but because it parses structure differently — and most prompt libraries never write for that difference at all.
Last updated Aug 15 · 11 min read
Why the same prompt performs differently across models
Claude responds measurably better to explicit structure — XML-style tags, imperative instructions, clearly stated output formats — than to a loosely worded conversational request. That is a real, documented behavioural difference, not a stylistic preference, which is exactly why a prompt written generically for "an AI assistant" and never adjusted for Claude specifically tends to underperform here compared with a model-specific one.
The Claude prompt library is written for that difference directly, not copy-pasted from a ChatGPT list with the model name swapped.
Projects: the persistence layer most people underuse
Claude Projects let you build a persistent knowledge base that stays accurate across many separate conversations, rather than re-explaining context every single chat. The library's prompt for setting up a Claude Project that stays accurate for months, not just this chat is built around the actual maintenance discipline that keeps a Project useful over time rather than becoming stale reference material nobody trusts anymore.
Artifacts: building something real, not a wall of text
Artifacts let Claude produce a self-contained, interactive output — a dashboard, a tool, a visualization — rather than a text description of one. The prompt for building a multi-view dashboard as one self-contained Claude Artifact is structured around getting a genuinely usable, multi-view result in one generation rather than iterating blindly toward it.
Long context: the real risk of "just paste the whole contract in"
Claude's long context window is a genuine strength, but pasting a full document in and asking a vague question wastes most of that advantage. The library's prompt for a long-context contract risk audit is structured to actually use the full context for a specific, thorough task — flagging risk clauses systematically — rather than a generic summarisation request that could have been done with a much shorter excerpt.
Safely ingesting untrusted content, and calibrating Extended Thinking
Two more technical prompts worth knowing about: XML-injection-safe content ingestion addresses a real, specific risk — since Claude's own structure relies on XML-style tags, content containing similar-looking tags needs careful handling so it cannot be mistaken for actual instructions. And Extended Thinking effort calibration helps you set the right reasoning depth for a task, rather than defaulting to maximum effort (slower, costlier) for something that did not need it, or too little for something that did.
Getting Claude to write in your actual voice
A recurring, real complaint about AI writing generally is that it reads as generically "AI-flavoured" regardless of the actual instruction given. The library's prompt for getting Claude to write in your actual voice, not the generic AI default works by feeding real writing samples rather than describing a tone abstractly — showing, not telling, which is measurably more effective at actually shifting output style.
When Claude needs to be part of a real product, not a chat window
These prompts cover using Claude directly, in a browser tab. Building Claude into an actual product or internal tool via the API — a support agent, a document-processing pipeline — is a different scope of work. That's exactly what Scult's AI agents team does, or book a meeting to talk through what that would take.
Need this built into your business?
The free tools and prompts on this site handle the small, solved problems. If what you need is bigger — AI agents & automation, built and maintained for you — that's Scult's day job.
