Prompt engineering is the work of writing, testing, and refining the instructions you give a large language model so it returns useful, consistent output instead of guesswork. We design the prompts behind real workflows: the system messages, the examples, the guardrails, and the structured outputs that make a model do a specific job the same way every time. The goal is a prompt you can rely on in production, not a clever one-off you typed into a chat box.
A good prompt is closer to a spec than a sentence. We figure out what the model actually needs to know, hand it the right context, show it a few examples of the answer you want, and then test it against the cases that tend to break. When the output has to feed another system, we make the model return clean JSON or a fixed format so your code can use it directly.
What’s involved
- Writing system and task prompts for a specific job, with examples and clear rules.
- Choosing the right model for the task, and the tradeoff between cost, speed, and quality.
- Structured outputs (JSON, tables, fixed fields) so results plug into your tools.
- Testing against the inputs that cause wrong answers, refusals, or made-up facts.
- Documenting the prompt so your team can change it later without breaking it.
We do this for our own agency operations: knowledge bases, reporting pulled from Search Console and analytics, and agent workflows we run day to day. So this is not theory. It’s the same prompt work we trust on our own business, applied to yours. If you only need one prompt fixed, that’s a small job. If you need prompts that hold up as part of a product, that’s where the testing and structure earn their keep.