Pakistan

Prompt Engineering Services

Good prompting is tested, not guessed

A prompt that works well on the three examples you tried manually can fail unpredictably on the range of real inputs your application actually encounters. We treat prompt engineering as a testing discipline: building a real set of test cases (including tricky edge cases) and iterating the prompt against measured performance, rather than tweaking wording until it "feels" right on a handful of manual tries.

What's included

A test case set built from your real expected inputs - typical cases and edge cases (ambiguous requests, adversarial inputs, unusual formatting)
Systematic prompt iteration measured against that test set, not subjective judgment on a few examples
Output format enforcement (structured JSON, specific response patterns) where your application needs reliably parseable output, not just readable text
Documentation of the final prompt design and why specific choices were made, so it can be maintained and adjusted by your team later

Our process

1. Define what "good output" means

Specific, measurable criteria - not just "sounds right" - so prompt quality can actually be evaluated rather than judged subjectively.

2. Build a real test case set

Covering typical and edge-case inputs your application will actually encounter, not just ideal examples.

3. Iterate systematically

Testing prompt variations against the full test set each time, tracking what actually improves versus what just changes the output.

4. Document the final design

So the reasoning behind the prompt is understood and maintainable, not a mysterious string of text that works for unclear reasons.

Pricing

Rs 30,000-150,000 depending on task complexity and how large a test case set is needed for reliable evaluation.

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Frequently asked questions

Isn't prompt engineering just trial and error?

Done well, no - we build a real test case set covering typical and edge-case inputs, and measure prompt changes against it systematically, rather than tweaking wording until a few manual examples look right.

Can you make the AI always return structured data (like JSON)?

Yes - output format enforcement is a common requirement when your application needs reliably parseable responses, not just readable text.

How do you know if a prompt is actually good?

Against specific, measurable criteria defined upfront and a real test case set - not subjective judgment on a handful of examples that may not represent real usage.

Will our team understand and be able to adjust the prompt later?

Yes - documentation covers the final design and reasoning behind key choices, not just a working string of text with unclear logic.

Is this only useful with OpenAI, or other models too?

The discipline applies to any LLM - we work with whichever provider your application actually uses.

What does it cost?

Rs 30,000-150,000 depending on task complexity and test case set size.

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