AI applied to agile work

Diplomas for the topic "AI applied to agile work" certify that their holder maintains an up-to-date and verifiable level of competence in the professional use of artificial intelligence within agile and software delivery environments. This diploma does not assess "theoretical knowledge" of AI, but rather the ability to apply AI with sound judgment, safety, and a results-oriented focus in everyday product and development tasks.

Certified competencies

Holding these up-to-date diplomas certifies that the holder is able to:

1) Structured, reusable prompting

Write clear, consistent prompts that incorporate objective, context, constraints, and output format, in order to obtain repeatable and useful results in real work.

2) Verification and reliability of responses

Evaluate the quality and trustworthiness of AI responses, requesting assumptions, evidence, or sources when appropriate, and avoiding decisions based on unverified information.

3) Separating instructions and data (anti prompt-injection)

Manage external inputs and potentially manipulated content, isolating data and instructions to reduce prompt-injection risks and stay in control of the task.

4) AI-assisted refinement: user stories and backlog

Improve backlog quality with AI support: identifying ambiguities, dependencies, and risks; splitting stories; and proposing refinement questions that facilitate estimation and delivery.

5) Testable acceptance criteria

Turn vague needs into verifiable criteria (including edge and negative cases), facilitating validation, testing, and alignment between business and team.

6) Designing hypotheses and experiments

Formulate hypotheses, define metrics and decision thresholds, and structure discovery/validation experiments to reduce uncertainty and avoid unfounded conclusions.

7) Generating and reviewing test cases

Use AI to propose and review test plans with a focus on coverage (negative cases, edges, regression) and gap detection before reaching production.

8) AI-assisted code review with a focus on risks

Apply AI as a support for reviewing code and changes, detecting inconsistencies, smells, and obvious risks (quality/security) and defining concrete actions before integration.

9) Privacy and handling of data/secrets

Apply good data minimization and protection practices when using AI: avoiding exposure of PII, credentials, or sensitive information; anonymizing when appropriate; and complying with confidentiality criteria.