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Impetora
Architecture

Prompt Engineering

Prompt engineering is the practice of designing, testing, and versioning the instructions given to a language model to elicit reliable, evaluable outputs.

What is Prompt Engineering?

A production prompt is not a one-off instruction. It is a versioned artefact with system context, role definition, output schema, examples, refusal conditions, and tool-use rules. Common techniques include chain-of-thought prompting, few-shot examples, self-consistency, structured output enforcement, and retrieval-grounded instructions. Prompts are evaluated with a held-out test set and tracked alongside model version, temperature, and tool descriptions, the same way code is.

How does Prompt Engineering apply to enterprise AI?

Enterprise prompt engineering replaces ad hoc 'try and tweak' work with versioned templates, evaluation harnesses, and rollback paths. This is required to satisfy EU AI Act technical documentation obligations.

Related terms

Foundational

Large Language Model

A Large Language Model (LLM) is a foundation model trained on text to predict the next token, capable of generating, summarising, and reasoning over natural language.
Production

Evaluation Harness

An evaluation harness is the test framework used to measure an AI system against a fixed set of inputs, expected outputs, and metrics, run on every change.
Production

Guardrails

Guardrails are runtime checks placed around an AI system to constrain inputs, outputs, and tool calls within safety, compliance, and business policy.

External references

Impetora

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