Prompt Engineering: Not Just for Code
Tech Writer
Same principles apply to docs, tutorials, and API references. Good prompts = better drafts.
Solutions Eng
Discovery questions, proposal outlines, demo scripts — all improve with better prompting.
Prompt Engineering: Not Just for Code
TL;DR
- Prompting well is a universal skill. It's how you steer any LLM, for any task.
- Good prompts = context + role + output format + constraints. Not magic phrases.
- The best prompters are clear thinkers. They know what they want before they type.
"Prompt engineering" sounds like a coding thing. It's not. It's the skill of giving AI enough context, direction, and structure so it produces something useful instead of something generic or wrong.
The Anatomy of a Decent Prompt
Think of it as a brief, not a magic spell. Every effective prompt has (most of) these elements:
- Context — What's the situation? "I'm debugging a Python service that times out under load."
- Role — What should the AI act as? "You're a senior SRE helping me trace a latency issue."
- Task — What exactly do you need? "Summarize these 3 log snippets and suggest the most likely root cause."
- Format — How should the output look? "Give me a bullet list, then one paragraph recommendation."
- Constraints — What to avoid? "Don't suggest we add more logging; we've already tried that."
You don't need all five every time. But the more ambiguous the task, the more of these you need.
The Big Mistake: Vague Requests
Bad: "Help me with this error."
Good: "I'm seeing this Java NullPointerException in production. Stack trace below. We're on Java 17, Spring Boot 3. No recent deploys. What are the 3 most likely causes?"
Bad: "Write something about our API."
Good: "Write a 2-paragraph intro for our REST API docs. Audience: external developers integrating with us. Tone: friendly but professional. Include that we support webhooks and rate limiting."
The gap isn't intelligence. It's specificity.
Prompting Patterns That Work Across Roles
| Pattern | When to Use | Example |
|---|---|---|
| Role + task | Any domain | "As a data architect, suggest a schema for event streaming with GDPR retention." |
| Few-shot | Output format matters | "Here are 2 examples of our incident summaries. Write one for this alert." |
| Chain of thought | Complex reasoning | "Think step by step: given these metrics, what's the most likely failure mode?" |
| Constraints first | Avoiding bad answers | "Don't recommend Kubernetes. We're on VMs. Suggest VM-based solutions only." |
These work for code, docs, analysis, support responses, and more.
When to Iterate vs. Start Over
First response bad? Often it's quicker to refine: "Same task, but shorter" or "Add a section on error handling." Don't assume the AI "failed." Assume the prompt was incomplete.
If you've iterated 3+ times and it's still off, restart with a cleaner prompt. Sometimes a fresh framing fixes it.
'Help me with this error.' 'Write something about our API.' 'Make this code better.' 'I need a status update.'
Click "Effective Prompt" to see the difference →
Quick Check
You ask AI: 'Write a test for this function.' The output is a generic unit test that doesn't match your testing framework. What's the most efficient fix?
Do This Next
- Rewrite one prompt you used recently. Add context, role, and output format. Run it again. Compare.
- Save 3 prompt templates for tasks you do often (e.g., "explain this error," "draft status update," "summarize meeting notes"). Reuse and refine them.