I Fed the Same Tech Copywriting Brief to ChatGPT, Claude & Gemini - The Results Will Shock You

With generative AI becoming a core tool in tech marketing, the question isn't "Can AI write?" - it's "Which AI writes the best tech content?"

I tested the latest versions of the top 3 AI models - ChatGPT, Claude, and Gemini - on real-world tech writing tasks.

From landing pages to onboarding emails, each model was put through the same set of prompts and judged based on these criteria:

• Clarity: Does anyone actually understand what you're selling?
• Tone: Does it sound like a human or a corporate automaton?
• Accuracy: Is this genuine or just smoke and mirrors?
• Persuasion: Will someone actually click that big, bright button to learn more, sign up, or purchase?

The tasks probe different aspects of copywriting competence: audience awareness, technical communication, tone calibration, and that ineffable quality we call "voice." Each one requires understanding not just what to say, but how to say it to a specific audience with particular expectations and tolerances.

Spoiler alert: each falls short in its own special way. But each is also surprisingly good at other tasks. Go figure.

Let's break it down.

How I Tested

I gave each AI the exact same 5 prompts crafted using my RITE prompting framework, all tailored for common tech copywriting tasks:

1. SaaS landing page hero text

2. Technical blog paragraph

3. Onboarding email

4. Developer FAQ response

5. LinkedIn product update post

I've kept the scoring simple: a 0–100% percentage per category, per AI model. Winner per task = highest average score.

Let's dive into the side-by-side results.

Task 1: SaaS Landing Page Intro

Here's what we're testing: Can these AI tools write homepage copy that doesn't sound like it was assembled by a committee of marketing robots? We need bold, confident, benefit-driven copy here.

The trick isn't just avoiding jargon - it's understanding that developers are skeptical by nature and can spot overhyped claims instantly.

Prompt:

Role: You're a tech copywriter who specializes in SaaS products and knows what makes developers actually click "sign up."

Information: Our product is "DeployMateX" - an AI-powered deployment automation platform for DevOps teams. It integrates with GitHub, GitLab, and Jenkins, and reduces deployment errors by 90%.

Task: Write the main headline and a 1-2 sentence subheadline for the homepage.

Extras: Tone should be bold, confident, and benefit-driven. No jargon. Keep it under 30 words total.

ChatGPT’s attempt

Deploy Faster, Fail Never

DeployMateX automates deployments to cut errors by 90%. Less debugging, more shipping—your code deserves better.

Clarity: 75%. Tone: 90%. Accuracy: 60%. Persuasion: 80%.

Verdict: Look, the headline's got pizzazz but lacks substance - kinda like a politician promising world peace without a plan.

"Fail Never" is pure hogwash and any dev worth their salt would roll their eyes so hard they'd see their own brain stem. That subheadline, though? Not too shabby.

Claude’s attempt

Ship code without the chaos

DeployMateX automates your deployments with AI, cutting errors by 90%. Deploy faster, sleep better, and stop firefighting broken releases.

Clarity: 85%. Tone: 90%. Accuracy: 80%. Persuasion: 85%.

Verdict: Claude nailed the headline like a carpenter with a new hammer - direct, satisfying, and exactly what devs crave. The copy speaks to the soul-crushing reality of 2AM production fires that every dev has experienced.

The only thing missing? A quick nod to which systems it integrates with. Dropping GitHub or Jenkins would've been the cherry on this otherwise tasty sundae.

Gemini’s attempt

Deploy Flawlessly.

Our AI automates your pipeline from GitHub, GitLab, and Jenkins, catching 90% of errors before they happen.

Clarity: 80%. Tone: 90%. Accuracy: 75%. Persuasion: 75%.

Verdict: The subheadline is where the meat is - it actually tells me what the product does, which puts it ahead of 90% of tech marketing already. Props for mentioning specific integrations too - that's catnip for skeptical devs who need to know if this fits their stack.

But that headline is vaguer than my aunt's "special ingredient" in her mystery casserole. "Deploy Flawlessly"? That's the beige wallpaper of headlines - inoffensive but uninspiring.

Winner: Claude

Task 2: Technical Blog Snippet

Now we're asking these digital scribes to explain vector embeddings to backend engineers. This is the kind of task that separates contenders from pretenders. You need to be pedagogical without being patronizing, technical without being obtuse.

The real test? Whether they can craft a metaphor that actually illuminates rather than obfuscates.

Prompt:

Role: You're a developer advocate writing a blog post for backend engineers who've been there, done that.

Information: Explain what "vector embeddings" are and how they're used in semantic search.

Task: Write a concise, beginner-friendly paragraph that defines embeddings and gives a simple example.

Extras: Avoid math. Use a metaphor or analogy. Assume the reader knows Python but not Machine Learning.

ChatGPT’s attempt

Vector embeddings are representations that translate words, sentences, or even entire documents into numerical vectors, capturing their meaning based on context and usage. Think of embeddings as placing words into a vast 3D space: similar concepts land close together, while unrelated terms drift apart. For example, if you had embeddings for "cat," "kitten," and "coffee," you'd expect "cat" and "kitten" to be neighbors, while "coffee" sits far away. In semantic search, this allows your application to quickly locate content that's conceptually relevant—even when exact keywords aren't matched—by comparing distances between embeddings.

Clarity: 90%. Tone: 95%. Accuracy: 95%. Persuasion: 85%.

Verdict: This explanation has the pellucidity that's rare in technical writing - it's crystal clear without dumbing things down. The spatial metaphor clicks instantly in your brain, and using the cat/kitten/coffee example was a stroke of genius that anchors the abstract concept.

The 3D space analogy works brilliantly even though it's technically a simplification (many embeddings are in thousands of dimensions).

Claude’s attempt

Think of vector embeddings as GPS coordinates for words and concepts in a multi-dimensional space. Just like how GPS coordinates (40.7128, -74.0060) uniquely identify New York City's location on Earth, embeddings are arrays of numbers like [0.2, -0.8, 0.1, ...] that capture the "meaning location" of text in a mathematical space. When you feed the word "dog" into an embedding model, it might output [0.3, 0.7, -0.2, ...], while "puppy" gets [0.31, 0.69, -0.18, ...] – notice how similar they are? That's the magic: semantically related concepts end up close together in this space. In semantic search, instead of matching exact keywords like traditional search, you convert both your query and documents into these coordinate arrays, then find the closest matches using distance calculations. So when someone searches for "canine," your system can find documents about "dogs" and "puppies" even though they never used that exact word.

Clarity: 85%. Tone: 90%. Accuracy: 85%. Persuasion: 95%.

Verdict: The GPS analogy is brilliant - who hasn't used Google Maps? It makes an esoteric concept feel familiar right off the bat.

There's a bit of awkwardness around "meaning location" - it's creative but feels like trying too hard to coin a term. It's also a tad wordy, but that verbosity actually helps rather than hurts in this case.

Gemini’s attempt

As a backend engineer, you know a computer sees "The dog is barking" as just a sequence of bytes. It has no idea what a 'dog' or 'barking' is. A vector embedding solves this. Think of it as a translator that converts any piece of data—like that text—into a list of numbers, a vector like [0.02, 0.91, -0.43, ...]. The magic is that this vector acts like a GPS coordinate for the meaning of the data. A special model places semantically similar concepts close together in a high-dimensional "meaning map." So, the vector for "The dog is barking" would be a very close neighbor to the vector for "The puppy is yapping," but miles away from "The stock market is crashing." Semantic search, then, is simply the act of finding the nearest numerical neighbors to your query's coordinate in that map.

Clarity: 85%. Tone: 90%. Accuracy: 90%. Persuasion: 85%.

Verdict: The opening line establishes immediate rapport with backend devs - "you know a computer sees..." creates that insider feeling that says "we're cut from the same cloth, you and I." The contrasting examples ("dog barking" vs "stock market crashing") are spot-on and intuitively graspable.

But Gemini jumps the gun by diving into "vector embedding" without laying groundwork. It's like a chef explaining a seasoning technique before telling you if you're cooking fish or dessert.

Winner: ChatGPT

Task 3: Onboarding Email for Dev Audience

This time we’ll evaluate a welcome email that's supposed to be professional but warm, direct but not robotic. This is where AI tools often reveal their true nature - they either go full corporate cheerleader or sound like they're addressing a room full of venture capitalists instead of humans who write code for a living.

The challenge isn't just hitting the word count; it's striking that delicate balance between being helpful and not sounding like you're trying to sell people a timeshare in Florida.

Prompt:

Role: You're a product marketer crafting onboarding emails that don't end up in the trash folder.

Information: The tool is "DocPilotX" - a platform that automatically generates and syncs developer documentation from codebases.

Task: Write a welcome email (under 150 words) for a developer who just signed up. Highlight two key benefits and include a friendly CTA.

Extras: Tone should be professional but warm. Avoid hype. No marketing fluff - be direct and clear.

ChatGPT’s attempt

Subject: Welcome to DocPilotX – Let's Get You Set Up

Hi [First Name],

Welcome aboard. DocPilotX is here to eliminate the grunt work from documentation.

Here's what you can expect right away:

- Automatic, real-time syncing of your documentation as your code changes.

- Cleaner, developer-friendly docs without manual formatting or upkeep.

Start by connecting your first repo — it takes less than a minute:

👉 [Connect Your Repo Now]

If you hit any bumps, reply to this email. We're here to help.

- The DocPilotX Team

Clarity: 90%. Tone: 85%. Accuracy: 80%. Persuasion: 80%.

Verdict: This email gets to the point faster than a caffeinated programmer on a deadline. The bullet points are clear as day and the overall vibe is refreshingly minimalist.

But yeesh, those specific promises about "less than a minute" setup and "real-time syncing" are setting themselves up for a world of hurt if the product doesn't deliver.

It ain't Shakespeare, but it's exactly what a dev needs after signing up.

Claude’s attempt

Subject: Your DocPilotX account is ready

Hi there,

Welcome to DocPilotX! Your account is set up and ready to go.

Here's what you can do right now:

Save hours every week – DocPilotX automatically generates documentation from your existing code, so you can focus on building instead of writing docs.

Keep everything in sync – When your code changes, your documentation updates automatically. No more outdated docs confusing your team or users.

Ready to connect your first repository? It takes about 2 minutes to see DocPilotX in action.

[Connect Your Repo →]

If you run into any questions, just reply to this email. I'm here to help.

Best,

Sarah

DocPilotX Team

P.S. Check out our [quick start guide] if you prefer to explore on your own first.

Clarity: 85%. Tone: 90%. Accuracy: 85%. Persuasion: 80%.

Verdict: This is well-structured and flows like a gentle stream - easy to follow without being pushy. The generic "Hi there" is a missed opportunity though - nothing says "mass email" like forgetting someone's name!

The "Save hours every week" opener is strong but veers into vagueland - how many hours exactly? Still, the tone hits that sweet spot between professional and approachable.

Gemini’s attempt

Subject: Welcome to DocPilotX

Hi [Name],

Welcome to DocPilotX! We're happy to have you.

Our goal is to eliminate documentation busywork so you can get back to coding. Here's how we help:

Automate Documentation: Stop writing docs from scratch. DocPilotX generates them directly from your codebase, saving your team hours.

Always Stay in Sync: Your documentation automatically updates with every commit, so it's always accurate and trustworthy.

The best way to see it in action is to connect your first repository. It only takes a minute.

[Connect Your First Repository]

Best,

The DocPilotX Team

Clarity: 85%. Tone: 80%. Accuracy: 90%. Persuasion: 70%.

Verdict: Short sentences, clear structure, easy skimming - this email ticks those boxes like a pro. There's zero fluff here, which devs appreciate.

The call-to-action is crystal clear too, but wow does it lack any emotional oomph. It's like asking someone to dance without playing any music! Where's the excitement? Where's the urgency?

Winner: Claude

Task 4: Developer FAQ Answer

Rate limiting explanations - now we're in the weeds where precision matters more than personality. This isn't about being clever; it's about being clear, concise, and actually helpful.

Most AI tools will either be too vague ("You'll experience throttling") or too verbose (a dissertation on HTTP status codes). The sweet spot is explaining the technical reality without making developers feel like they're reading a legal document.

Prompt:

Role: You're writing technical documentation for an AI API platform, and you know developers hate vague answers.

Information: The platform limits requests to 100 RPM (requests per minute). Developers often ask: "What happens if I exceed the limit?"

Task: Write a short FAQ answer that explains the rate limit, what happens when it's exceeded, and how to handle retries.

Extras: Keep it to 3-5 sentences. Include a best practice if relevant. Tone: helpful, concise, no filler.

ChatGPT’s attempt

Q: What happens if I exceed the 100 requests per minute (RPM) limit?

A: If you exceed the 100 RPM limit, the API will return a 429 Too Many Requests error. The response includes a Retry-After header indicating when to retry. To handle this, implement exponential backoff with jitter to avoid thundering herd issues. Best practice: throttle requests client-side and monitor usage to stay within limits.

Clarity: 90%. Tone: 95%. Accuracy: 90%. Persuasion: 85%.

Verdict: This answer cuts to the chase like a hot knife through butter - direct, professional, no wasted words. The status code details are spot-on, and I appreciate the mention of the Retry-After header that many docs mysteriously omit.

But hang on a sec - it drops "jitter" and "thundering herd" like everyone should know what those are! A one-line explanation would've made this perfect.

Claude’s attempt

Q: What happens if I exceed the 100 RPM rate limit?

A: When you exceed 100 requests per minute, the API returns a 429 Too Many Requests status code with a Retry-After header indicating how long to wait before retrying. Your request is rejected and not processed, so you'll need to implement retry logic in your application. The rate limit resets every 60 seconds from your first request in the current window. Best practice: Implement exponential backoff for retries and consider spreading requests evenly throughout the minute rather than bursting all 100 requests at once.

Clarity: 95%. Tone: 95%. Accuracy: 95%. Persuasion: 80%.

Verdict: This is technical writing that doesn't make me want to gouge my eyes out - a rare achievement! Everything's explained with surgical precision, and I love that it explicitly states the requests are rejected, not queued (a common misunderstanding).

The reset explanation is the only wobble - "from your first request in the current window" is a bit muddled. Solid stuff, though.

Gemini’s attempt

Q: What happens if I go over the rate limit?

A: Our API enforces a limit of 100 requests per minute (RPM). If you exceed this limit, your subsequent requests will be rejected with a 429 Too Many Requests HTTP status code until the rate window resets. To handle this, implement a retry mechanism with exponential backoff, which is a standard strategy for gracefully handling rate-limiting errors. Avoid retrying immediately, as this can prolong the issue.

Clarity: 85%. Tone: 90%. Accuracy: 90%. Persuasion: 80%.

Verdict: This answer does what it says on the tin and the warning against immediate retries is a thoughtful inclusion that shows they've actually seen people make this mistake (I certainly have).

But it's missing some key details - when exactly does the "rate window" reset? The beginning of each minute? Rolling? These aren't nitpicks - they're the difference between a smooth implementation and a late-night debugging session that makes you question your career choices.

Winner: Claude

Task 5: LinkedIn Announcement Post

CodeSynthX v2.0 just added Rust, Go, and Dockerfile support. So, let's draft a LinkedIn post under 100 words using a casual-professional tone, targeting backend engineers.

This is where AI tools often faceplant hardest - they either sound like press releases written by robots or try so hard to be "casual" that they end up sounding like that one marketing person who uses "fam" unironically.

Prompt:

Role: You're a tech product manager writing a LinkedIn post that won't make developers roll their eyes.

Information: Your team just launched v2.0 of "CodeSynthX," a code suggestion AI that now supports Rust, Go, and Dockerfile contexts.

Task: Write a LinkedIn post (under 100 words) announcing the update. Emphasize how this helps backend engineers move faster.

Extras: Use a casual-professional tone. Add a short, punchy sentence that invites devs to try it or comment.

ChatGPT’s attempt

CodeSynthX v2.0 just dropped — now with support for Rust, Go, and Dockerfile contexts. That means faster backend builds, smarter suggestions, and less tab-switching to docs.

We built this to get out of your way and help you ship with confidence.

🚀 Backend devs: give it a spin and let us know where it actually helps — or doesn't.

Clarity: 80%. Tone: 85%. Accuracy: 75%. Persuasion: 80%.

Verdict: This post hits a sweet spot between professional and casual - it feels like something a real human would actually write. The value props are clear and relevant to daily dev life (that tab-switching callout is spot-on - I probably switch tabs 500 times a day).

But there's a vague black hole where specifics should be - "get out of your way and help you ship with confidence" could mean literally anything.

Claude’s attempt

Just shipped CodeSynthX v2.0 with Rust, Go, and Dockerfile support 🚀

No fluff here – this actually helps backend engineers move faster by understanding context across your entire stack. Whether you're optimizing Rust performance, spinning up Go services, or containerizing deployments, it gets the nuances right.

Our beta users are seeing 40% faster iteration cycles when jumping between languages and infrastructure code. The AI finally speaks backend engineer, not just generic autocomplete.

Drop a comment if you want early access, or tell me what language we should tackle next.

#BackendEngineering #DeveloperTools #Rust #Golang

Clarity: 85%. Tone: 90%. Accuracy: 75%. Persuasion: 80%.

Verdict: Starting with "No fluff here" is like saying "I'm not like other marketers" - a bold move that actually pays off because the post delivers on that promise. The specificity around use cases (optimizing Rust, spinning up Go services) tells me exactly how this fits into my workflow.

The hashtags are a bit much though - feels like someone's grandpa discovered social media.

Gemini’s attempt

Big update for backend devs: CodeSynthX v2.0 is live.

We heard you. You can now get AI-powered code suggestions for Rust, Go, and Dockerfiles. The goal is simple: spend less time wrestling with boilerplate or context-switching and more time shipping solid code. It's about moving faster without the friction.

Take it for a spin. What should we support next?

Clarity: 85%. Tone: 90%. Accuracy: 85%. Persuasion: 80%.

Verdict: This post has the confident swagger of someone who knows their audience. "We heard you" acknowledges that this update came from user feedback, which is pure gold to developers who want to feel listened to. The "wrestling with boilerplate" line speaks directly to a pain point that makes me want to bang my head against the wall daily.

But the CTA? Weaker than gas station coffee. Overall, though, it captures that elusive casual-professional tone without trying too hard - no small feat.

Winner: Gemini

Conclusion: Which AI Should You Use?

Claude earns the first spot, but there's no single winner across the board. Each model shines in specific content types:

Use Claude for polished, structured, persuasive content (landing pages, onboarding, product copy).

Use ChatGPT for high-clarity technical writing (blogs, articles, whitepapers).

Use Gemini for creative, informal content (social, casual explainer posts).

Pro tip: Run the same prompt across all three tools and merge the best ideas. It's like having three different expert writers on your team - each with their own strengths - but you get to be the editor who cherry-picks the best bits.

Your competitors are probably still debating whether to use AI at all, while you're already combining the best of all worlds. That's the kind of unfair advantage small businesses dream about – and it’s yours for the taking.