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 copy session by pulling the raw product facts from the source document or API. Those facts include specifications, use cases, target audience notes, and any existing marketing material. I copy them into a plain‑text note so I can see everything without formatting distractions. Then I run a quick mental checklist: does the note contain a clear statement of what the product does, who it helps, and why it matters? If any of those three pieces are missing, I add them before moving on.
Next I transform the raw facts into a benefit‑led description. I write a short paragraph that leads with the primary outcome the user gets, followed by secondary outcomes. I keep the language active and avoid passive constructions. After the paragraph I add a set of scannable bullets. Each bullet starts with a strong verb and ends with a measurable result or a concrete advantage. I limit each bullet to one idea and keep the length under 20 words where possible.
Finally I run the draft through the eight product‑copy rules. I read each rule aloud and ask myself if the draft satisfies it. If a rule fails, I edit the specific sentence or bullet that caused the failure. I repeat the pass until all eight rules pass. Only then do I consider the copy ready for publishing. I never push copy live without this final verification step.
When I receive a new product brief I first look for the core value proposition. I ask: what problem does the product solve, and what measurable improvement does it deliver? I write that answer in one sentence. For example, if the product is a spreadsheet automation tool I might write: “It reduces manual data entry time by 70 % for finance teams.” That sentence becomes the anchor for the rest of the copy.
Next I collect supporting facts. I list every feature, every integration, every compliance certification, and every user‑test result. I put each fact on its own line in a markdown file. I then tag each line with a category: feature, benefit, audience, or proof. Tagging helps me see gaps quickly. If I see no proof tags I know I need to ask for a case study or a test result before I can claim any performance numbers.
I also check external sources for credibility. For security‑related claims I consult the OWASP Top 10 for Large Language Model Applications (https://owasp.org/www-project-top-10-for-large-language-model-applications) to make sure I am not inadvertently suggesting the product mitigates a risk it does not cover. For AI‑risk claims I refer to the NIST AI Risk Management Framework (https://www.nist.gov/itl/ai-risk-management-framework) to align my language with recognized guidelines. Those links appear only as references; I do not copy any text from them.
After I have a complete, tagged list I delete any duplicate or contradictory entries. I keep only the strongest proof points for each benefit. This cleaned list becomes the input for the drafting stage.
I turn each tagged benefit line into a bullet that follows a simple pattern: Verb + Object + Result. The verb must convey action (e.g., “reduces”, “accelerates”, “eliminates”). The object is what the user interacts with (e.g., “manual data entry”, “report generation time”). The result is a quantifiable outcome or a clear advantage (e.g., “by 70 %”, “to under five minutes”, “without extra licensing”).
Here is how I process a sample benefit line:
benefit: reduces manual data entry time by 70 % for finance teamsI repeat this for every benefit tag. I then read the bullets aloud to check rhythm. If two bullets start with the same verb I vary the verb where possible (e.g., replace “reduces” with “cuts” or “lowers”). I also ensure that no bullet exceeds 20 words; if it does I split the idea into two bullets.
After the bullets are ready I place them directly under the opening paragraph. I keep a blank line between the paragraph and the first bullet list to improve visual scanning. I do not add any decorative icons or special characters; plain text bullets work best in email clients and CMS fields.
Finally I add a short lead‑in sentence before the bullets that states the main outcome in plain language. That sentence is not a bullet; it serves as a quick glance for readers who prefer a paragraph format.
I have refined these eight rules over dozens of copy cycles. Each rule targets a common weakness in product descriptions. I list them here with a brief explanation and a concrete example of a violation and a fix.
Violation: “Our software has a drag‑and‑drop interface.”
Fix: “Build reports in half the time with a drag‑and‑drop interface.”
Violation: “Data is processed by the engine.”
Fix: “The engine processes data in real time.”
Violation: “Leverages synergistic APIs.”
Fix: “Connects to your existing tools through standard APIs.”
Violation: “Improves speed significantly.”
Fix: “Cuts processing latency from 200 ms to 30 ms.”
Violation: “Our solution provides a comprehensive suite of features that enable users to manage, analyze, and visualize their data across multiple sources while ensuring security and compliance.”
Fix: “Manage, analyze, and visualize data from multiple sources with built‑in security and compliance.”
Violation (for a novice audience): “Utilizes advanced machine learning algorithms to optimize workflow orchestration.”
Fix: “Uses smart automation to simplify your daily tasks.”
Violation: “Actually, basically, the product really helps you to basically get things done faster.”
Fix: “The product helps you get things done faster.”
Violation: “Customers love our product.”
Fix: “92 % of surveyed customers reported a reduction in manual work after three months.”
I keep this list in a sticky note on my desktop. Before I finalize any copy I run through each rule, marking a pass or fail. If any rule fails I edit the offending line immediately.
To make the review process visible I copy the draft into a simple HTML table. The left column shows the original sentence or bullet, the middle column lists which rules it violates, and the right column shows the revised version. I use this table only for my own review; I never publish it.
| Original | Violated rules | Revised |
|---|---|---|
| Our platform offers a powerful analytics engine. | 1, 4 | Gain instant insight into sales trends with our analytics engine. |
| Data is encrypted at rest and in transit. | 2 | The platform encrypts data at rest and in transit. |
| It uses cutting‑edge technology to deliver results. | 3, 6 | It uses standard technology to deliver results fast. |
| Users see a big improvement in productivity. | 4 | Users report a 35 % increase in weekly productivity. |
| Actually, the solution basically works really well. | 7 | The solution works well. |
I fill the table row by row. When I see a rule number in the middle column I know exactly what to fix. After I update the right column I copy the revised text back into the main draft. I repeat the table until the middle column is empty for every row, meaning all eight rules pass.
I treat product copy like code. I keep a Git repository for each product line. The repository contains three folders: raw for the source facts, drafts for each iteration, and published for the final version that goes live.
When I start a new copy cycle I create a branch named copy/YYYY-MM-DD-product-slug. I add the raw facts to raw/facts.txt. I then write the first draft into drafts/v0.txt. After each pass through the eight rules I commit a new version: v1.txt, v2.txt, and so on. The commit message notes which rules were fixed in that step (e.g., “fixed rules 2 and 5”).
If a stakeholder requests a change I open a pull request from the feature branch to main. The PR shows the diff between the last published version and the new draft. Reviewers can comment on specific lines, and I can address those comments by adding more commits. Once approved I squash‑merge the branch and move the final file to published/copy.txt. I also tag the commit with a version number like copy-v1.2.0.
This approach gives me an audit trail. If a published claim later needs verification I can check the commit that introduced it and see which proof point was used at that time. It also prevents accidental overwrites; the published folder always contains the exact copy that went live.
I host the repository on a private Git service. I enable branch protection rules that require at least one approving review before merging. This mirrors the decision‑support nature of ProdCopy: I never publish without a human confirming the final version.
This guide is not a promise that every product description will convert visitors into buyers. It does not guarantee higher rankings in search results. It does not claim to eliminate the need for a designer or a marketer. It is a decision‑support tool that helps you produce clearer, more scannable copy based on a repeatable process.
I do not assert that the eight rules are universally applicable to every industry or every format. They emerged from my experience with SaaS product pages and may need adjustment for long‑form white papers, technical specifications, or regulatory filings. You should test the output with your target audience and adjust as needed.
The workflow described here does not replace user research, usability testing, or analytics review. It works best when you already have a clear understanding of the problem your product solves and the evidence that supports your claims. If you lack that foundation, spend time gathering it before running the copy process.
Finally, this is not a substitute for legal review. If your copy includes claims about compliance, security, or performance that could have legal implications, have your legal team verify those statements before publishing.
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