by ProdCopy (LX AI)
ProdCopy runs a real product-copy workflow: extract value, draft benefit-led description with scannable bullets, then refine against eight product-copy rules before you publish.
Decision-support only — confirm before you publish or act.
I start each test by cloning the live product page into a staging environment. The clone keeps the same URL structure but points to a separate database so any changes do not affect real traffic. I then create two variants: Variant A uses the existing copy tone, Variant B receives the alternate tone generated by the workflow. Traffic is split 50/50 using a feature flag that reads a cookie set on first visit. I log impressions, clicks, and add‑to‑cart events for each variant in a simple Postgres table.
Before launching the split I run a sanity check: I verify that the staging site loads within two seconds on a mid‑tier mobile device, that the alternate tone does not break any schema markup, and that the call‑to‑action button remains visible above the fold. Only after these checks do I enable the flag for a random 10 % of users to monitor for anomalies. If error rates stay below 0.1 % I raise the allocation to the full 50/50 split.
The first stage of the workflow pulls raw attributes from the product feed: title, brand, material, dimensions, power rating, and any highlighted features. I store these attributes in a JSON object that the next stage can query. I then run a short Python script that removes duplicates, normalises units (e.g., converting “12 in” to “30.48 cm”), and flags missing required fields.
After cleaning, I ask the model to list the top three customer benefits that each attribute supports. For example, a “stainless‑steel blade” might yield benefits like “resists rust”, “keeps sharp edge longer”, and “easy to clean”. I keep the output as a plain‑text list so I can manually verify that each benefit is grounded in a concrete attribute.
With the cleaned attribute‑benefit map I prompt the model to produce five benefit‑focused bullets. Each bullet must start with a verb, mention the attribute, and end with a customer outcome. I enforce a maximum length of 120 characters per bullet to keep them scannable on mobile screens.
I then run a quick readability check: I count syllables, aim for a Flesch‑Kincaid grade level between 6 and 8, and ensure that no bullet contains more than two commas. If a bullet fails any of these checks I ask the model to rewrite it with a simpler verb or shorter phrase.
After the bullets are generated I place them in a temporary HTML snippet and view them on a range of devices (iPhone SE, Pixel 4a, iPad Mini) to confirm that line breaks do not cause awkward wrapping.
The workflow ends with a rule‑based polish stage. I have encoded eight rules as simple regex patterns and corresponding replacement actions. The table below shows each rule, its purpose, and an example of a before‑and‑after transformation.
| Rule ID | Description | Before example | After example |
|---|---|---|---|
| 1 | Remove passive voice | The blade is made of stainless steel. | The blade uses stainless steel. |
| 2 | Limit adverb use to one per sentence | The blade cuts very smoothly and very quietly. | The blade cuts smoothly and quietly. |
| 3 | Replace vague adjectives with concrete terms | High‑quality material | 304‑grade stainless steel |
| 4 | Ensure each sentence has a subject and a verb | Durable and long‑lasting. | The blade is durable and long‑lasting. |
| 5 | Avoid exclamation marks in body copy | Amazing performance! | Amazing performance |
| 6 | Use numerals for numbers above nine | Five year warranty | 5‑year warranty |
| 7 | Spell out units on first use, then abbreviate | 12 in blade | 12 in (30.48 cm) blade |
| 8 | End benefit statements with a customer gain | Blade resists rust | Blade resists rust so you spend less time cleaning |
I apply the rules in the order shown, because later rules sometimes depend on earlier changes (e.g., rule 7 needs the unit to be present before it can abbreviate). After the final pass I run a quick spell‑check and then store the polished copy in the variant B slot of the staging database.
This workflow does not guarantee a higher conversion rate. It does not promise that every test will produce a statistically significant lift. It does not claim to replace a full copy‑editing team or to eliminate the need for human judgment. The output is a decision‑support tool: you still review the generated copy, you still run the A/B test, and you still decide whether to ship the variant based on your own data.
I avoid language that suggests certainty. I do not use words like “always”, “never”, or “100 %”. I treat each run as an experiment that can fail, and I log both successes and failures to improve the prompting strategy over time.
Before I expose the alternate tone to users I run two safety layers. First I check the generated text against the OWASP LLM Top 10 list of vulnerabilities, focusing on prompt injection and data leakage risks. I submit the copy to the https://owasp.org/www-project-top-10-for-large-language-model-applications scanner and verify that no finding exceeds a low severity rating.
Second I align the workflow with the NIST AI Risk Management Framework. I document the model’s intended use, the data sources, and the mitigation steps I have taken (temperature setting, token limits, and human‑in‑the‑loop review). I record these artifacts in the https://www.nist.gov/itl/ai-risk-management-framework portal for internal audit.
If either layer flags a concern I halt the test, review the prompt, and adjust the constraints before proceeding. This extra step adds roughly five minutes to the preparation cycle but helps catch issues that could affect brand trust or user safety.
I keep a written checklist next to every release that touches this topic. The list is boring on purpose: name the owner, name the evidence artifact, and name the rollback. If a step cannot produce a file, a ticket, or a log line, it is not a step — it is a wish.
| Signal | What to record | Stop-ship? |
|---|---|---|
| Unexpected write or send | Tool name, args, identity | Yes until gated |
| Missing confirmation on destructive action | Call path and bypass | Yes |
| Untrusted text treated as instruction | Source surface and resulting action | Yes for agent reach |
| Scanner clean, judgment open | Deferred items + owner | Only if core path safe |
prodcopy helps with the measurable first pass. Architecture choices, legal sign-off, and customer-facing claims still need a human owner. Treat every checklist row as decision support, not a certificate.
When a prospect asks whether the product "covers everything," I answer with the boundary: automated checks catch known patterns; novel chains and policy judgment still need review. That honesty keeps support load down and keeps the blog aligned with what the product actually does.
If you need a second opinion, pair this write-up with the references below and with your own staging reproduction. Do not skip the reproduction step just because a model wrote a confident summary.
ProdCopy runs a real product-copy workflow: extract value, draft benefit-led description with scannable bullets, then refine against eight product-copy rules before you publish.
Decision-support only — confirm before you publish or act.
Want to try the product? ProdCopy
Generated 2026-09-17 · primary sources only · no fabricated traffic metrics