Prompt Engineering
Engineered, tested, and optimized prompts so your AI behaves exactly as needed.
Prompt engineering and LLM optimization. We engineer, test, and tune system prompts and model configurations for chatbots, agents, and AI workflows — so your AI behaves exactly the way you need.
What you get
Who needs prompt engineering?
Prompt engineering is for teams with an AI feature whose behaviour needs to be made explicit, tested, and repeatable. It fits chatbots, agents, extraction or classification flows, and generative experiences where an informal prompt no longer gives dependable enough output.
What does prompt tuning deliver?
The work can include system prompts, model and tool configuration, representative test cases, evaluation criteria, failure and refusal behaviour, and documented changes. The deliverable is the configured behaviour and the evidence used to assess it, not a single piece of prompt text in isolation.
How do you evaluate whether a prompt is better?
We test against representative inputs and agreed expectations, then inspect accuracy, format, source use, tool calls, refusals, and failure cases relevant to the workflow. A change is useful when it improves those real tasks, not simply when one demonstration looks better.
Can prompts be integrated into an existing product?
Yes. We tune prompts in the context of the model, retrieval layer, tools, product states, and safeguards that call them. For Alibaba Cloud Malaysia, DayDreamer integrated and tuned a system prompt around WAN video models inside a public web experience.
How are test data and sensitive inputs handled?
We agree which examples may be used, who can access them, which model services process them, and what should be excluded or anonymised. Any regulatory, retention, or data-residency requirement must be identified before the evaluation set and model configuration are agreed.
Who owns the prompts, the evaluation set, and the tuning work?
Ownership is set out explicitly in the project agreement before work starts, and the agreement names each part rather than leaving it to assumption: your own data and examples, the prompts and system instructions written for you, the evaluation set and test cases built to show that a change is an improvement, and any configuration produced by the tuning work — as against pre-existing or third-party components, which stay under their own licences. It also settles what comes across at handover — the prompts, the evaluation set, the model and tool configuration, and the record of what changed and why — together with who may run, change, and extend them afterwards. That is what decides whether your own team can keep testing and adjusting the behaviour once the engagement ends, so it is named alongside the deliverables above instead of being worked out at the end.
When is prompt engineering not enough?
Prompt changes are not enough when the model lacks the required information, the retrieval source is poor, a tool or integration is missing, or the task needs a deterministic rule rather than a model judgment. We should fix the data, architecture, or workflow instead of endlessly rewriting the prompt.
How we work
Daydream
We scope your idea and define what 'good' looks like.
Design
We shape the experience and architecture before writing code.
Build
We engineer AI-native software with intelligence woven in.
Ship
We launch on scalable cloud, then measure and refine.
Proof
Frequently asked questions
Does prompt engineering include testing and evaluation?+
Yes. We define representative test cases and expected behaviour, then assess output, source use, tool calls, refusals, and failure cases rather than judging a prompt from one demonstration.
Can DayDreamer tune prompts inside an existing AI product?+
Yes. We work with the model, retrieval layer, tools, product states, and safeguards around the prompt so the configured behaviour fits the live workflow.
Can every AI problem be fixed with a better prompt?+
No. If the model lacks information, retrieval is weak, an integration is missing, or the task needs deterministic rules, the data, architecture, or workflow needs to change instead.
Related services
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