Data, AI & Analytics
Applied AI & Prompt Engineering skills assessment
AI practitioners build useful, safe applications on top of large language models and other AI tools. This scorecard covers prompt design, retrieval, evaluation, safety and integration for teams shipping AI features.
- 10 skills to score
- 1–10 scale on every skill
- 5 starting levels
The 10 Applied AI & Prompt Engineering skills you can score
These are the default Applied AI & Prompt Engineering skills in SkillScorer. Each one is scored from 1 to 10 for every analyst, and you can edit, remove or add skills at any time.
Use Case Selection and Scoping
Chooses problems where AI adds value and defines what good looks like.
Prompt Design and Iteration
Writes clear, testable prompts and improves them with evidence.
Retrieval and Grounding Techniques
Uses retrieval and context design to reduce errors and hallucination.
Evaluation and Testing of AI Outputs
Builds evaluation sets and measures quality, accuracy and regressions.
Safety, Privacy and Misuse Prevention
Protects data, limits harmful outputs and manages prompt injection risks.
Integrating AI into Products and Workflows
Connects models to systems, APIs and user interfaces reliably.
Cost, Latency and Model Selection
Balances model choice, speed and spend.
Human-in-the-Loop Design
Designs review steps and fallbacks where accuracy matters most.
Monitoring and Continuous Improvement
Monitors production behaviour and user feedback and iterates.
Responsible AI and Governance
Applies organisational policy, transparency and regulatory expectations.
Levels included with Applied AI & Prompt Engineering
Applied AI & Prompt Engineering starts with 5 levels, which SkillScorer calls “Seniority Level” for data, ai & analytics, so analysts can be grouped and compared fairly. Rename, reorder or replace them at any time.
- Associate
- AI Engineer
- Senior AI Engineer
- Lead
- Head of AI
How Applied AI & Prompt Engineering works in SkillScorer
Score
Each analyst in a team is scored on the Applied AI & Prompt Engineering skills in the Skills Matrix: tap a cell, type a score, and it saves itself.
Compare
Rank analysts, compare two side by side, or compare your teams against each other to see exactly where the strengths and gaps are.
Track
Scores are kept year after year, so you can see who is improving and which Applied AI & Prompt Engineering skills need attention. Reports also follow each team lead's team.
Applied AI & Prompt Engineering scorecard: common questions
What skills does the Applied AI & Prompt Engineering scorecard include?
SkillScorer's Applied AI & Prompt Engineering scorecard covers 10 skills: Use Case Selection and Scoping, Prompt Design and Iteration, Retrieval and Grounding Techniques, Evaluation and Testing of AI Outputs, Safety, Privacy and Misuse Prevention, Integrating AI into Products and Workflows, Cost, Latency and Model Selection, Human-in-the-Loop Design, Monitoring and Continuous Improvement and Responsible AI and Governance. Each one comes with a written description of what it measures, and you can edit, rename, remove or add skills to suit your organisation.
How are analysts scored in Applied AI & Prompt Engineering?
Every analyst is scored from 1 to 10 on each skill (1 = Poor, 5 = Average, 10 = Elite), one team and year at a time. Scores autosave in the Skills Matrix, and the 18 built-in reports turn them into rankings, radar charts, skill gaps and year-on-year trends.
Which levels come with Applied AI & Prompt Engineering?
Applied AI & Prompt Engineering starts with these levels (called “Seniority Level” in the app): Associate, AI Engineer, Senior AI Engineer, Lead and Head of AI. They are just a starting point, so you can rename, reorder or replace them.
Can I change the Applied AI & Prompt Engineering skills and wording?
Yes. The skills, levels and labels are all editable per organisation. In data, ai & analytics the app talks about analysts, teams and team leads, and you can change any of those words too.
Score Applied AI & Prompt Engineering with SkillScorer
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