Prompt Engineering & AI Skills Training

The Gap Between Deploying AI and Using It Well

Organisations can deploy capable AI tools and still see limited adoption. The barrier is rarely technical — it is the gap between having access to an AI system and knowing how to direct it effectively. Staff who have not been taught to work with AI produce mediocre results, grow frustrated, and revert to previous workflows. The AI investment goes underutilised.

Prompt engineering training closes this gap. It is the practical skill of communicating with AI systems precisely enough to get reliable, useful output — and critically evaluating that output before acting on it.

What the Training Covers

Fundamentals of Effective Prompting. How AI language models interpret instructions, why prompt phrasing affects output quality, and the structural techniques — context setting, role assignment, output specification, chain-of-thought prompting — that consistently improve results. Participants practise with the AI tools their organisation has deployed, not generic examples.

Task-Specific Prompt Patterns. We develop prompt libraries tailored to the participant's role: policy analysts learn patterns for synthesising regulatory text; lawyers learn patterns for document review and legal research; financial analysts learn patterns for data interpretation and report drafting. These libraries become a shared organisational resource.

Critical Evaluation of AI Output. The most important skill we teach is not prompting — it is the ability to evaluate what the AI produces. Participants learn to identify factual errors, logical inconsistencies, inappropriate confidence, and cases where the AI has misunderstood the task. Professional judgement, applied to AI output, is the safeguard that makes AI safe to use in consequential work.

Managing AI in Professional Workflows. How to integrate AI tools into existing work processes without creating new risks — including appropriate disclosure when AI has contributed to a work product, version control of prompts used for recurring tasks, and escalation procedures when AI output is uncertain.

Information Security and Data Handling. What information may and may not be entered into AI systems, particularly cloud-hosted tools. For government clients, this maps directly to the classification of information under the Policy on Government Security and the Access to Information Act. For legal clients, this includes solicitor-client privilege implications of using AI tools with third-party providers.

The Legal Sector Context

The legal profession faces particular obligations around the use of AI — competence obligations, accuracy requirements, and privilege considerations. Our training for legal professionals addresses these directly. Participants learn not only how to use AI tools but how to document their use appropriately, verify AI-assisted legal research, and comply with Law Society guidance on AI in legal practice.

The Government Context

Federal departments using AI tools must navigate the Directive on Automated Decision-Making, the Policy on Government Security, and obligations under the Access to Information Act and Privacy Act. We incorporate these requirements into training content so that staff understand not just how to use AI but the legal and policy boundaries within which they must use it.

For departments with bilingual service obligations, we address the particular challenges of using AI tools for content that will be used in both official languages — including quality verification requirements for AI-assisted translation or drafting.

Delivery Formats

Training is available in full-day workshops, half-day intensive sessions, and multi-week cohort programmes with practice assignments between sessions. We recommend the cohort format for organisations seeking sustained behaviour change rather than a single training event. All formats include hands-on practice with the AI tools participants will use in their roles.