Using ChatGPT to Write Product Descriptions That Don’t Backfire

Using ChatGPT to Write Product Descriptions That Don’t Backfire

ByUS Stacked

1. Reusing an old prompt meant for a different brand voice

I reused a dusty old ChatGPT prompt I’d originally written for a DTC skincare startup. (It had emojis, exclamation points, and way too many words like “refreshing” and “glow.”) Then I pasted that into a product description workflow for an industrial safety gear client. You can imagine how that went. Getting “ Heavy-Duty Protection Boots – Stay Safe in Style ” was, uh, not what the client had in mind.

The sneaky thing is: OpenAI quietly improved tone handling on their backend around late 2023. Prompt behavior shifted without notice. The same prompt now leans more into the reference examples you include. Which also meant that my embedded tone example warped the results even further — every description read like a lifestyle Instagram caption.

If you write prompts assuming consistency over time… nope. Revalidate every few months or cue up backups using summaries instead of tone instructions. I now ask ChatGPT to analyze a batch of real listings and reapply patterns, instead of dictating tone manually.

2. Why system messages sometimes get silently ignored mid-stream

ChatGPT’s Custom GPTs and API workflows let you use a system message to “set the rules” — but if your payload bundles user and system instructions too closely, the model can ignore earlier guidance completely. That happened when I passed this structure:

{
  messages: [
    { role: 'system', content: 'Write product descriptions in a concise tone' },
    { role: 'user', content: 'Describe this flashlight' }
  ]
}

Worked fine the first time through. Then I inserted a structured product schema — JSON fields like feature_bullets, use_case, and tagline — and got responses with generic sales fluff instead of using my inputs. The model treated the JSON as primary context and deprioritized the prior system prompt.

There’s no visible warning or flag. It just shifts behavior midstream. What fixed it: sandwiching a reflective step. I started sending my own structured summary to the system prompt first — like, “You are helping write technical B2B product descriptions. Use the traits described below.” Then nesting the inputs. That re-centered the tone logic before the actual description starts.

3. Character count limits are real but not where you expect them

I thought I was playing it safe by slicing product descriptions to 800 characters — Shopify caps the field around 1000, so that left a buffer. Turns out, trimming the content wasn’t enough. ChatGPT was inserting invisible UTF characters when generating bolded phrases like “Key Features” or symbols like bullets.

What showed up in the API logs:

description_characters: 798
actual_transmitted_characters: 1016

Yep. Those lovely smart quotes and auto-bullets turned into multi-byte sequences that blew through the limit.

Fixing it brute-force style:

  • Strip all “smart” punctuation: curly quotes, em dashes, ellipses
  • Unformat Unicode-alike symbols to plain text equivalents
  • Run a raw Buffer.byteLength() check, not string.length

It wasn’t until I piped the output through a raw character sanitizer (basically a bad regex soup) that I saw the actual byte count settle under control.

4. The unexpected bias when generating copy from spreadsheets

So I thought: if I give ChatGPT 200 rows of existing product info and descriptions, it’ll spot patterns. For example, I had a column labeled “Primary Use,” another called “Tagline,” and a final column with human-written descriptions. Then I asked the model to write new descriptions by learning from that dataset.

It worked — except it also heavily overfit to the first 5 rows. I didn’t realize until a friend pointed out: “Why do they all start with ‘Whether you’re at home or on-site’?”

After digging into it, I learned the model was associating phrases with cell positions, not content types. If the first few rows included safety toe boots, every new one echoed their sentence structures — even for gloves and eyewear.

I got better results by:

  • Randomizing the row order before feeding examples
  • Flattening vocabulary across the dataset to reduce outlier dominance
  • Explicitly labeling parts of text (e.g., [TAGLINE]:)

An “aha” moment came when I tried this post-processing prompt: “Analyze the variety of opening phrases in the sample set. Highlight phrases used more than twice.” It flagged eight repeated intros that I hadn’t mentally connected. Bias snuck right in while I was tuning for speed.

5. When ChatGPT descriptions depend on vague product titles

You’d think “SteelMax 2400” would be enough. Turns out, not even close. Both in GPT-3.5 and GPT-4, ChatGPT will invent product use cases if the title doesn’t clearly describe the function — and those hallucinations are subtle enough that they don’t trigger as wrong until a specialist reads it.

For example, a battery-powered heater with no timer settings got this in the description:

“…includes an automatic timer so your space is cozy when you arrive.”

Nobody ever mentioned a timer. The model assumed it based on similar product titles in vague categories.

Workaround: always pass a structured feature object with every request. I use this format now:

{
  product_title: "SteelMax 2400 BTU Heater",
  features: [
    "Battery-powered",
    "No timer included",
    "Outdoor rated",
    "Max runtime: 6 hours"
  ]
}

Then in the prompt I add: “Only reference listed features. Do not assume any others.” Doesn’t stop 100 percent of hallucination, but it drops the error rate to nearly zero — and the model even stops trying to metaphorize product names.

6. Why outputs shift dramatically when regenerating mid-thread

This one caught me while batch-generating 75 descriptions via API. If the same prompt is reused in a loop and an error crashes after prompt 46, retrying from prompt 47 forward doesn’t always yield comparable results.

I traced this down to entropy handling in the model’s random seed logic. When you use the same system prompt repeatedly in a loop and feed similar inputs, the model builds a kind of invisible context “heat” in thread memory — but only within full conversations. If you start mid-thread with only partial config, GPT-4 generates different syntactic patterns than before. Sentences suddenly lengthen. Synonyms change. Passive voice appears more often.

It’s subtle. You won’t notice unless you compare prompts 1–46 and 47–75 side-by-side.

Handling it without full conversations:

  • Use the seed parameter in OpenAI’s API
  • Hardcode phrase starters if consistency is needed
  • Post-process sentence structure using an external linter or style guide assistant

The inconsistency looked like a tone shift — but underneath, it was just the absence of shared prompt history causing variance in determinism.

7. Rate limiting and the quiet throttling of descriptive tone

OpenAI won’t always hard-fail under rate limits. If you’re brushing against too many requests per minute, GPT may respond slower or trim response tokens — without saying why. Which is how I ended up with some product descriptions getting cutoff mid-metaphor like this:

“Built for all-terrain endurance, the X-Ridge 700 is your go-to choice when…”

Then just… nothing. No response error. The result field just ended.

I combed request logs and saw that only longer descriptions were doing this. Turned out, requests hovered near token limits, and GPT fell back to an incomplete close (not enough budget to finish the paragraph). Lowering max_tokens fixed it — temporarily.

Eventually I switched to streaming output mode, capturing chunks manually so I could flag premature cutoffs in realtime and retry intelligently when the API failed quietly. Not for everyone, but if you’re batching lots of content at scale… don’t trust a missing period to mean “complete.”