Glossary · Definition
Prompt engineering
Prompt engineering is designing the instructions, examples and context given to a language model so it performs a task reliably.
By Nythrex EngineeringUpdated 1 min read
In plain words
The same model can give very different results depending on how the task is described. Good prompts state the goal, the constraints, the output format and a few examples — and are improved against a test set, not by intuition.
When it matters to you
- Every LLM feature, as the first and cheapest lever
- Before considering fine-tuning
- When output format or tone needs to be consistent
Common pitfalls
- Endless tweaking without a test set
- Prompts that grow into unmaintainable rulebooks
- Treating prompt rules as security boundaries
Keep reading
GuideLLM evals for non-ML teamsHow to evaluate an LLM feature without a data-science team: build a test set, choose metrics, use LLM-as-judge carefully, run evals in CI and read the results.GlossaryFine-tuningFine-tuning is further training of an existing model on your own input–output examples to make its behaviour — format, style, classification — more consistent.GlossaryGuardrailsGuardrails are the checks and limits around an AI system — on inputs, outputs and actions — that keep it within its intended scope and policies.
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