Prompt engineering
Prompt engineering that survives real users.
Production prompt engineering is the discipline of making an AI feature behave the same way on the ten-thousandth request as on the first. The Super Platform writes, tests, versions and monitors prompts the way engineers handle code.
From clever prompt to reliable system
A prompt that works in a demo often fails on messy inputs, long documents or hostile users. Reliability comes from structure: a clear system prompt, examples, retrieved context, and an output schema the application can check.
- System prompts with explicit rules and refusals
- Structured outputs validated against a schema
- Few-shot examples taken from real cases
- Retrieval of the right context at the right time
Measured, versioned, regression tested
Every prompt has an evaluation set. A change ships only if it does not break cases that used to pass. Prompts live in version control with the code, so any answer can be traced to the exact prompt that produced it.
Read prompt engineering for production for the full approach.
Cost, latency and safety
We size the model to the task, cache what can be cached and keep prompts short enough to stay fast. Inputs from users and documents are treated as untrusted, following the OWASP guidance for LLM applications.
Frequently asked questions
Frequently asked questions
Is prompt engineering still needed with newer models?
Yes. Newer models follow instructions better, but a production feature still needs explicit rules, structured outputs, evaluation and protection against injected instructions. The work shifts from wording tricks to system design.
Can you improve prompts we already use?
Yes. We start by building an evaluation set from your real traffic, measure the current prompts against it, then improve them with each change proven by the numbers.
How do you defend against prompt injection?
Untrusted content is separated from instructions, tool permissions are kept minimal, outputs are validated before use, and sensitive actions require human approval. No single measure is enough, so they are layered.
Blog
Further reading
Prompt engineering for production: build reliable AI systems
How to turn clever prompts into reliable AI features: structured outputs, examples, context engineering, evaluation sets, versioning, cost control and injection defense.
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