The jargon eliminator: turn engineering docs into a sales-ready explanation
Why this prompt matters
Sales and customer-success teams that can't accurately translate technical capabilities into plain language either oversell (creating support tickets and churn when reality doesn't match the pitch) or undersell (losing deals to a competitor whose team explained the same feature with more confidence). A single garbled technical explanation in an enterprise sales call can stall a deal by weeks while procurement seeks clarification from engineering directly.
What we use it for
Your engineering team just shipped a new rate-limiting change to the API, and your account executive has a call with a Fortune 500 procurement team in two hours who will ask what this means for their integration timeline and reliability.
Prompt
Act as a senior technical writer who specializes in translating complex engineering and product documentation for non-technical stakeholders without losing accuracy or making misleading simplifications. CONTEXT: Original document: [PASTE YOUR TECHNICAL DOCUMENT OR EXCERPT HERE] Original audience: [WHO THIS WAS WRITTEN FOR, e.g. "backend engineers" or "DevOps team"] Target audience: [WHO NEEDS TO UNDERSTAND IT NOW, e.g. "enterprise sales team" or "non-technical executives"] What they need to do with this information: [SPECIFIC GOAL, e.g. "explain this feature to a customer during a sales call" or "approve a budget request based on this capability"] TASK: Rewrite the document for the target audience above. Preserve every fact, number, and constraint from the original — you are translating the language, not the content. Where a concept genuinely requires a technical term, keep the term but add a one-line plain-English explanation next to its first use. CONSTRAINTS: - Do not oversimplify to the point where a follow-up question would reveal your explanation was misleading - Keep exact numbers, version numbers, dates, and technical thresholds unchanged - Do not use metaphors or analogies that break down under a single follow-up question — test each one mentally before using it - Do not remove caveats, limitations, or edge cases that were in the original — restate them in plain language instead - Target reading level: educated professional with zero domain-specific background, not a general consumer audience OUTPUT FORMAT: 1. **Rewritten Document** — the full translated version, same structure as the original (same headers/sections) 2. **Jargon Glossary** — a table with three columns: Original Term | Plain-English Explanation | Why It Matters (one line each) 3. **Questions This Might Raise** — 3-5 follow-up questions a non-technical reader would likely ask after reading, each with a one-sentence answer, so the target audience can pre-empt them in conversation Length target: [DESIRED LENGTH, e.g. "under 400 words" or "match original length"]
Result
Input (excerpt from an engineering doc): "The API implements token bucket rate limiting with a refill rate of 100 requests/minute and a burst capacity of 250. On 429 responses, clients should implement exponential backoff with jitter, starting at 500ms, capped at 30s, with a maximum of 5 retry attempts before surfacing a hard failure to the caller."
1. Rewritten Document:
Our API limits how many requests a customer's system can send us to keep the service fast and reliable for everyone. Think of it like a parking garage with 250 spaces: customers can burst in quickly up to that limit, but the garage only lets in 100 new cars per minute on average after that. If a customer sends requests too fast and gets turned away, their system is designed to automatically wait a moment and try again — starting with a half-second pause, and waiting longer each time it's turned away again, up to a maximum 30-second wait. After 5 tries, if it still hasn't gotten through, the customer's system will show an error instead of waiting forever. This means customers rarely notice rate limiting during normal use — it only becomes visible if they're sending an unusually high volume of requests at once.
2. Jargon Glossary:
| Original Term | Plain-English Explanation | Why It Matters |
|---|---|---|
| Token bucket rate limiting | A system that allows quick bursts of activity but caps the average rate over time | Customers can burst up to 250 requests instantly, not just 100/minute flat |
| 429 response | An error code meaning "you're sending requests too fast, please slow down" | This is expected behavior, not a bug — customers' systems should handle it automatically |
| Exponential backoff with jitter | Waiting progressively longer between retries, with small random variation | Prevents thousands of customer systems from all retrying at the exact same moment and overwhelming us again |
3. Questions This Might Raise:
- Will customers see errors during normal use? No — 250 requests in a burst covers virtually all normal usage patterns; this only triggers under sustained high volume.
- What happens after 5 failed retries? The customer's application shows a clear error rather than hanging indefinitely, so their team can investigate.
- Can we raise these limits for a specific customer? Yes, rate limits are configurable per API key — that's a conversation for the account team, not an engineering blocker.
- Does this slow down the API for everyone else? No — it protects overall system performance, which is why average response times stay consistent even during traffic spikes.
Every growing company hits the same translation problem: the people who build the product speak a different language than the people who sell it, support it, or fund it. Engineering docs are precise but impenetrable to a sales rep prepping for a call in twenty minutes. Marketing copy is readable but often drifts from what the product actually does. This prompt is built to sit exactly between those two failure modes.
Why this prompt is structured the way it is
The Role instruction — "technical writer," not "marketer" or "copywriter" — matters more than it looks. A marketer optimizes for excitement; a technical writer optimizes for accuracy that a non-expert can still follow. That distinction is what keeps the output honest instead of glossy.
The Context section forces four specific inputs: the source document, who wrote it, who needs to read it now, and what they'll do with the information. Skipping any of these produces generic, unusable output. "Explain this simply" without knowing the audience's goal produces an explanation that's simple but doesn't answer the question the reader actually has.
The Constraints section does the real work of preventing bad output. "Do not oversimplify to the point where a follow-up question would reveal your explanation was misleading" is the single most important line in the prompt — it's what stops the model from producing a metaphor that sounds clean but falls apart the moment someone asks a real question about it. Requiring exact numbers and caveats to survive the rewrite prevents the common failure mode where a simplified explanation quietly drops the exception that mattered.
Why the output format includes a glossary and anticipated questions
Most jargon-simplification prompts stop at the rewritten paragraph. This one deliberately doesn't, because the rewritten paragraph alone doesn't prepare someone for a live conversation. The glossary table gives the reader a fast reference if a term comes up again later. The "questions this might raise" section is the highest-value part of the output: it anticipates what a smart non-expert will ask next and pre-loads the answer, so the reader walks into a meeting prepared rather than improvising under pressure.
How to adapt it
The prompt works for any audience gap: engineering-to-sales, legal-to-product, finance-to-everyone. Swap the target audience and goal fields, and the same structure holds. For very long source documents, run it section by section rather than all at once — the model handles focused, complete sections more reliably than a single pass over ten pages of dense material.