El eliminador de tecnicismos: convierte documentos de ingeniería en una explicación lista para ventas
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.
Toda empresa en crecimiento se topa con el mismo problema de traducción: quienes construyen el producto hablan un idioma distinto a quienes lo venden, lo soportan o lo financian. Los documentos de ingeniería son precisos pero impenetrables para un representante de ventas que se prepara para una llamada en veinte minutos. El copy de marketing es legible pero a menudo se aleja de lo que realmente hace el producto. Este prompt está diseñado para ubicarse exactamente entre esos dos modos de falla.
Por qué este prompt está estructurado así
La instrucción de Rol — "redactor técnico", no "mercadólogo" ni "copywriter" — importa más de lo que parece. Un mercadólogo optimiza para generar emoción; un redactor técnico optimiza para precisión que un no experto pueda seguir. Esa distinción es la que mantiene el resultado honesto en lugar de brilloso.
La sección de Contexto exige cuatro entradas específicas: el documento fuente, quién lo escribió, quién necesita leerlo ahora y qué harán con la información. Saltarse cualquiera de estos produce un resultado genérico e inutilizable. "Explícalo simplemente" sin saber el objetivo de la audiencia produce una explicación simple pero que no responde la pregunta que realmente tiene el lector.
La sección de Restricciones hace el trabajo real de prevenir malos resultados. "No simplifiques al punto de que una pregunta de seguimiento revele que tu explicación era engañosa" es la línea más importante del prompt — evita que el modelo produzca una metáfora que suena limpia pero se desmorona en cuanto alguien hace una pregunta real al respecto. Exigir que los números exactos y las salvedades sobrevivan a la reescritura previene el modo de falla común donde una explicación simplificada omite silenciosamente la excepción que importaba.
Por qué el formato de output incluye un glosario y preguntas anticipadas
La mayoría de los prompts para simplificar jerga se detienen en el párrafo reescrito. Este deliberadamente no lo hace, porque el párrafo reescrito por sí solo no prepara a alguien para una conversación en vivo. La tabla del glosario le da al lector una referencia rápida si un término vuelve a aparecer más tarde. La sección de "preguntas que esto podría generar" es la parte de mayor valor del output: anticipa lo que un no experto inteligente preguntará a continuación y precarga la respuesta, para que el lector entre a una reunión preparado en lugar de improvisar bajo presión.
Cómo adaptarlo
El prompt funciona para cualquier brecha de audiencia: ingeniería a ventas, legal a producto, finanzas a todos. Intercambia los campos de audiencia objetivo y objetivo, y la misma estructura se mantiene. Para documentos fuente muy largos, ejecútalo sección por sección en lugar de todo a la vez — el modelo maneja secciones completas y enfocadas de manera más confiable que un solo pase sobre diez páginas de material denso.