Glossary
Prompt engineering
Designing the instructions, examples and structure that shape model behaviour.
In production this is software engineering: prompts are versioned, tested and reviewed like any other code, because an untested prompt edit is an untested deployment.
Examples usually outperform instructions. Showing the model three correct outputs beats describing correctness in a paragraph.
Version control is the practice that separates production systems from prototypes. When a prompt lives in a file, moves through review and carries tests, a quality regression is traceable to a commit. When it lives in a database field someone edited on Friday, it is not.
Treat examples as the highest-value part of the prompt. Three correct outputs communicate a format more reliably than a paragraph describing it, and they survive model changes better than carefully tuned instructions do.
Commonly misunderstood: Treating prompts as strings someone edits in production is the single most common source of unexplained quality regressions.
Related terms, in context
The concepts you almost always meet alongside prompt engineering.
- Evaluation
- A labelled test set that turns 'it seems good' into a number you can regress against.
- Structured output
- Constraining a model to return data matching a schema, so downstream code can rely on it.
- Context window
- How much text a model can consider at once, measured in tokens.
Where this shows up in our work
Prompt engineering is not an abstraction for us. It is a decision we make on live projects. It shows up most directly in llm application development, where getting it wrong has a cost someone can measure.
If you are evaluating a vendor on this, the useful question is not whether they can define the term. It is what they measure, what they would refuse to do, and what happens in their system when the assumption behind prompt engineering stops holding.
Questions
What is Prompt engineering?
Designing the instructions, examples and structure that shape model behaviour.
What do people get wrong about prompt engineering?
Treating prompts as strings someone edits in production is the single most common source of unexplained quality regressions.
Does Orqent Labs build this?
Yes, LLM Application Development. We work across India, covering all 19,238 PIN codes remotely.
Building something that involves prompt engineering?
We will tell you honestly whether it is the right approach for your problem.
Or email bd@dtrasglobal.com · call +91 74118 77878