Data, Analytics & AI

Skill areas

AI & Automation

AI Prompt Engineer

An AI Prompt Engineer designs, tests, and refines the instructions — known as prompts — that guide large language models (LLMs) to produce accurate, relevant, and safe outputs for a specific use case. Day-to-day work involves writing and iterating on prompts for production AI applications, running structured evaluations to measure how well prompts perform against defined quality criteria, collaborating with software engineers and product managers to integrate AI outputs into products and workflows, and documenting prompt strategies, failure modes, and evaluation results. The role requires both technical curiosity — understanding how LLMs behave and why — and strong written communication skills, since the primary tool is language itself.

Career information

Everything you need to know about this role

Salary, eligibility, background, qualifications, and where people typically work.

Expected salary range

P25 and P75 are the 25th and 75th percentiles for UK full-time pay (national benchmarks). UK median is the national full-time median salary.

P25UK medianP75

£29,000£39,000£54,000

Source: ONS Annual Survey of Hours and Earnings (ASHE), UK full-time gross annual earnings, April 2025 (released Oct 2025).

Entry

0–2 yrs

£35,000–£55,000

Mid

2–5 yrs

£55,000–£85,000

Senior

5+ yrs

£85,000–£130,000

Dot = average of the range; coloured bar shows min–max vs UK benchmarks above.

Eligibility limits

Right-to-work, checks, and role-specific requirements — tap to view.

Right to work in the UK required. No formal licence or registration required. Some roles involve access to sensitive data used in AI training or evaluation, which may involve enhanced background checks. Roles at government or defence AI projects may require SC clearance.

Understand eligibility rules →

How the role developed

The role emerged from the realisation that the same underlying model can produce dramatically different outputs depending on how it is instructed — and that designing those instructions well is a distinct skill that combines knowledge of how LLMs work, clear communication, and systematic experimentation. Early LLM applications in 2022 and 2023 were often brittle because prompts were written informally; as organisations moved from prototype to production, the need for rigorous prompt design and evaluation became clear.

Current direction

The UK has a significant AI sector, with London ranking among the top three global cities for AI investment. Regulatory interest in AI is high — the UK government's AI Safety Institute, formed in 2023, and the EU AI Act, which applies to UK firms deploying AI in the EU, have both increased attention to safe and reliable AI outputs, which prompt engineering directly addresses.

The field is maturing rapidly: techniques like chain-of-thought prompting, few-shot learning, retrieval-augmented generation, and structured output generation are now well-established, and the role is increasingly expected to span from prompt design through to evaluation methodology and deployment considerations.

Back to Data, Analytics & AI or all industries.

technicalai-prompt-engineer

Why they ask

Evaluation is what separates professional prompt engineering from informal experimentation. This tests whether candidates can build a systematic quality assurance process for AI outputs.

What they want

A structured approach covering what to evaluate, how to measure it, how to automate measurement, and how to use the results to iterate on prompts.

How to structure your answer

Define the quality criteria for the specific application
Design a test set of representative inputs
Choose evaluation methods — human raters, LLM-as-judge, automated metrics
Describe how to run and track evaluations over prompt iterations
Connect evaluation to the prompt iteration process

Sample answer

I would start by defining what good looks like for this specific application — accuracy, format adherence, tone, factual grounding, safety — because the right evaluation framework depends entirely on what matters. For a customer support summarisation tool, accuracy and completeness matter most; for a creative writing assistant, originality and tone matter more. Next I would build a golden test set: a representative sample of inputs with expected or reference outputs, ideally covering common cases, edge cases, and known failure modes. Evaluation methods depend on what can be automated. For structured tasks — classification, extraction, format compliance — I can write automated checks in Python that compare the output against expected values. For more subjective quality — helpfulness, coherence, safety — I would use LLM-as-judge, where a second model scores the outputs against defined rubrics; this scales better than human evaluation but needs to be calibrated against human judgment periodically. I would track evaluation results in a structured log alongside the specific prompt version, model version, and temperature settings that produced them, so changes in quality can be traced back to specific changes. Every prompt iteration gets evaluated before being promoted to production, and I would set thresholds — minimum acceptable accuracy or safety scores — that gate deployment.

Common mistakes

Describing evaluation as just "testing a few examples and seeing if they look good" — that is not an evaluation framework
Not mentioning LLM-as-judge — it is the most scalable evaluation approach for subjective quality dimensions
No mention of regression testing — ensuring a prompt change that improves one dimension does not degrade another

Bonus tips

Reference tools like LangSmith, Braintrust, or PromptFlow as evaluation platforms that are gaining adoption
Mention that the test set should be representative — biased toward easy cases will produce misleadingly high evaluation scores
Note that evaluation is ongoing, not one-time — models are updated by providers and can change behaviour without a deliberate prompt change

AI Prompt Engineer

Everything you need to know about this role

Expected salary range

Eligibility limits

How the role developed

Current direction

Everything you need to know about this role

Expected salary range

Eligibility limits

How the role developed

Current direction

Where this role can take you

Overview

Why this path matters now

Sample career paths

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