Prompting for LLMs: ChatGPT, Claude & Gemini — What Actually Differs
The same prompt sent to ChatGPT, Claude, and Gemini will produce three noticeably different responses. Not slightly different — structurally different. Different lengths, different tones, different assumptions about what you want. Understanding those differences is the difference between getting a first draft you can use and getting something you have to rewrite from scratch.
This article covers the practical behavioral differences between the three major LLMs and how to adjust your prompts to get better results from each. It's written from regular use of all three — not from documentation.
Default behavior: ChatGPT defaults to structured, comprehensive responses. Given a vague prompt, it will produce a well-organised answer that covers multiple angles — usually more than you asked for. It formats heavily with headers, bullets, and numbered lists. It tends toward thoroughness over conciseness.
What it does well: Long-form structured writing (reports, proposals, outlines), iterative refinement over multiple turns, following complex multi-step instructions, code generation, and tasks where you need a comprehensive starting point you'll edit down.
What to watch for: GPT-4o's default output is often too structured for conversational copy. "Write a casual Instagram caption" will often produce something that reads like a press release unless you're explicit about format. Add "no headers, no bullet points, conversational tone" to any copy request.
Prompting tips: GPT models respond well to role definitions ("You are a senior copywriter") and explicit format instructions. Use system prompts to set persistent behavior across a session. It handles very long context well — you can paste an entire brand document as context and it will use it throughout the conversation.
Default behavior: Claude defaults to thoughtful, measured responses with a distinct voice. It tends toward prose over bullets and will often push back gently if it thinks your framing is wrong. It's the most willing of the three to disagree with your premise — which is occasionally annoying and frequently useful.
What it does well: Copy with a specific tone or voice, nuanced writing tasks where register matters, analysis that requires judgment rather than just information retrieval, long-context tasks (Claude has one of the largest context windows available), and any task where you want the model to think carefully before answering.
What to watch for: Claude can be verbose in its reasoning. It will often explain why it's about to do something before doing it. If you want just the output with no preamble, add "Do not explain your process. Output only." to the prompt. It also tends to hedge on definitive statements — useful for accuracy, frustrating for confident marketing copy.
Prompting tips: Claude responds exceptionally well to examples. Show it a piece of writing you like and ask it to match the register — it will read the style more accurately than any other model. For copy tasks, give it the brand voice description and 1–2 reference pieces. It will produce output that's closer to on-brand than GPT without needing to specify every constraint.
Default behavior: Gemini Flash is optimized for speed and broad competence across task types. It tends toward efficient, direct responses — less verbose than GPT, less stylistically distinctive than Claude. Its strongest differentiator is speed and its integration with Google's information ecosystem.
What it does well: Research tasks that benefit from recent information, quick summaries, multimodal analysis (images, documents, audio), and tasks where you need a fast first draft rather than a polished final output. Gemini 3.5 Flash in particular is extremely fast — useful for high-volume tasks where you're processing many prompts in sequence.
What to watch for: Gemini's creative writing output tends to be more generic than the other two. For brand copy or content with a specific voice, GPT or Claude will usually produce better first drafts. Use Gemini for speed and research; use the others for quality creative output.
Prompting tips: Gemini responds well to direct, simple instructions. It doesn't need as much scaffolding as GPT for basic tasks. For research prompts, adding "Based on the most current available information" improves response quality. For multimodal tasks (analyzing an image or document), Gemini's integration makes it the fastest option.
Universal Prompt Techniques That Work Across All Three
Role Definition
All three models respond significantly better when given a specific role. "You are a senior copywriter at a luxury fashion brand" outperforms "write copy for a luxury fashion brand" on every model. The role activates a specific vocabulary, tone, and decision-making frame that improves output quality without requiring more instructions.
Explicit Format Instructions
Left to their own devices, all three models will choose a format. That choice is rarely the one you want. Always specify: word count, structure (prose vs bullets vs headers), length of individual sections, and what not to include ("no introductory sentence explaining what you're about to do").
Negative Constraints
Tell the model what to avoid, not just what to do. All three models have strong default tendencies that negative constraints can override. "No corporate jargon. No exclamation marks. Never use the word 'innovative'." is more useful than any positive instruction you could write to replace those patterns.
Chain-of-Thought for Complex Tasks
"Think step by step before answering" improves accuracy on reasoning, analysis, and planning tasks across all three models. It forces the model to surface its logic before committing to an answer — which catches errors the model would otherwise skip past.
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