What is prompt engineering?
Prompt engineering is the practice of writing, structuring and testing the instructions given to an AI model so that it produces accurate, consistent output for a specific task. In a product, prompt engineering works like programming: the prompt is versioned, tested and changed with care.
Why it matters
In many AI features, the prompt decides more about quality than the choice of model. A clear prompt with examples and rules cuts wrong answers and can let a smaller, cheaper model do the job. Treating prompts as tested code also means a model update does not quietly break your product.
1What goes inA role, the task, the rules, examples of good answers and the exact output format, such as JSON.
2Test, do not guessRun each prompt change against a fixed set of real cases and compare the results before it goes live.
3Prompting vs fine-tuningPrompting changes the instructions; fine-tuning changes the model itself and is worth it only after prompts reach their limits.
ExampleAn insurance broker in Almaty uses an AI model to sort incoming emails and messages into claims, renewals and complaints. The first prompt mixes up renewals and complaints. After adding definitions, a few real examples per category and a strict output format, the team tests it on last month’s emails and puts it live.
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Related terms
LLMA large language model: an AI model trained on huge amounts of text to read and write language.Fine-tuningFurther training of an existing AI model on your own examples so it handles one task in a set way.RAGA way to make an AI model answer from your own documents by fetching the relevant passages first.AI hallucinationWhen an AI model states something false or invented as confidently as if it were a fact.
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