Generative AI - Maximising The Benefits with Prompt Engineering

1. Insight

Generative AI tools such as large language models do not think, reason, or understand in the way humans do. They respond to prompts — instructions provided by users — and the quality of those prompts has a direct impact on the quality, reliability, and usefulness of the outputs generated.

Prompt engineering is the discipline of designing clear, specific, and well-structured prompts to achieve a desired outcome from a generative AI system. It is not a technical skill reserved for specialists. It is a practical capability that leaders, managers, and staff can learn and apply immediately.

Effective prompting:

• Sets a clear task

• Provides the right context

• Uses references where appropriate

• Evaluates the output critically

• Iterates to improve results

  
  

Prompting is rarely a one-off activity. It is an iterative process that improves outcomes over time.

In this insight, we introduce a simple, tool-agnostic prompting framework suitable for common organisational use cases, including:

1. Creating content

2. Summarising information

3. Clarifying complex material

4. Extracting key data

5. Translating text

6. Editing and refining outputs

7. Supporting problem-solving and decision-making

A clear, structured prompting approach helps organisations get better, more consistent output from AI tools while reducing rework and variability.

2. Why This Matters

Many organisations are already using Generative AI, but results vary significantly between teams and individuals. The difference is often not the tool itself, but how it is used.

For organisations, effective prompt engineering: maximises the benefits of generative AI since it:

• Unlocks greater productivity and quality from AI tools

• Reduces inefficiency and rework

• Improves consistency of outputs

• Helps mitigate operational and governance risks

• Prevents loss of competitive advantage through poor or uncontrolled usage

Without a shared approach to prompting, AI use can become fragmented, unreliable, and risky — undermining trust and slowing adoption.

3. Prompt Engineering to Maximise Benefits of Generative AI

A structured prompting approach is not just helpful — it is essential at organisational scale. Without it, teams rely on trial and error, leading to inconsistent results and frustration.

A simple framework gives staff:

• A repeatable method they can apply to any task

• Confidence in using AI responsibly

• Better outcomes with less effort

  
  

3.1 Prompt Engineering Framework

The following framework is widely recommended across industry and is deliberately tool-agnostic, making it applicable to any generative AI system and any type of output (text, images, code, audio, or video).

It follows a simple, logical flow: Task → Context → Reference → Evaluate → Iterate

This structure keeps prompts focused, grounded, and adaptable.

3.2 Task

What is the AI being asked to do?

The task should be stated clearly and unambiguously. Vague prompts lead to vague outputs.

Example: Instead of: “Help me with a report”

Use: “Draft a one-page executive summary outlining the key risks and opportunities of adopting Generative AI in a regulated organisation.”

Benefits

• Improves relevance and clarity

• Reduces unnecessary output

• Aligns results with business needs

  
  

3.3 Context

What background does the AI need to produce a useful response?

Context may include:

• The audience

• The organisation or sector

• Constraints such as tone, length, or format

Example: “The audience is senior leaders with limited technical knowledge. Use plain language and focus on decision-making implications.”

Benefits

• Tailors outputs to the intended user

• Improves tone and suitability

• Reduces the need for rework

  
  

3.4 Reference

What source material or standards should the AI use?

References anchor the output and reduce the risk of generic or misleading content.

Examples

• Internal policies or documents

• Regulatory guidance

• Existing reports or frameworks

Benefits

• Improves accuracy and alignment

• Reduces hallucination risk

• Ensures consistency with organisational standards

3.5 Evaluate

Generative AI outputs should always be reviewed critically.

Key evaluation questions include:

• Is the output accurate?

• Is the output unbiased?

• Does it provide sufficient information?

• Is it relevant to my task or project?

• Is the output broadly consistent when the same prompt is reused?

Evaluation is a user responsibility, not something to delegate to the tool.

3.6 Iterate

Prompting is rarely perfect on the first attempt.

Iteration involves:

• Refining wording

• Adding or removing context

• Clarifying constraints

• Narrowing or expanding scope

Benefits

• Progressive improvement in quality

• Better alignment with business intent

• Faster learning across teams

3.7 Rolling This All Together

The most effective users experiment deliberately. They:

• Try alternative phrasing

• Adjust levels of detail

• Test different instructions

Small changes in wording can produce materially different outcomes. Over time, this builds confidence and competence.

3.8 Text Generation - Example Prompt

Use Case - Drafting an executive briefing note.

Example Prompt

Task - Draft a one-page executive briefing note outlining the key benefits and risks of adopting Generative AI within a regulated organisation.

Context - The audience is senior executives with limited technical knowledge. Use plain language, a professional tone, and focus on strategic and operational implications rather than technical detail.

Reference - Base the content on common enterprise AI considerations, including governance, compliance, productivity, and data protection best practices.

3.9 Image Generation - Example Prompt

Use Case - Creating a professional visual for a consulting presentation.

Example Prompt

Task - Create a clean, professional infographic illustrating a five-step prompt engineering framework.

Context - The image will be used in a corporate consulting presentation. It should appear modern, minimal, and suitable for senior business audiences.

Reference - Use a horizontal layout with five connected steps labelled: Task, Context, Reference, Evaluate, Iterate. Apply a muted blue corporate colour palette and simple line icons.

3.10 Audio and Video Generation

Use Case - Producing a short leadership explainer video script.

Example Prompt

Task - Write a 60-second script for a leadership explainer video introducing prompt engineering and why it matters to organisations.

Context - The video will be delivered by a senior leader and aimed at managers and professionals. The tone should be confident, clear, and reassuring rather than technical or sales-driven.

Reference - Structure the script with: A brief opening hook A simple definition of prompt engineering One practical organisational benefit A clear closing message

3.11 Code Generation

Use Case - Creating a simple internal utility script.

Example Prompt

Task - Generate a Python script that reads a CSV file and produces a summary report showing total records, missing values per column, and basic descriptive statistics.

Context - The script will be used by analysts with intermediate Python knowledge. Readability and clear comments are more important than optimisation.

Reference - Use standard Python libraries such as pandas and numpy. Follow common Python coding conventions and include inline comments.

3.12 Key Takeaway

Across all use cases, strong prompts are structured prompts. By explicitly defining the task, supplying context, grounding the request with references, and critically evaluating outputs, organisations can significantly improve:

• Output quality

• Consistency

• Efficiency

• Trust in AI-enabled ways of working

These examples are deliberately tool-agnostic and can be applied across current and future Generative AI platforms.

3.13 Techniques for Prompting

Several prompting techniques can further improve results:

• Few-shot prompting - Multiple examples are included, helping the AI recognise patterns and produce more consistent outputs. Particularly useful for structured or repetitive tasks.

• Chain-of-thought prompting - The prompt explicitly asks the AI to explain its reasoning step by step. This can improve transparency and decision support.

• Prompt chaining - Outputs from one prompt are fed into another, allowing complex tasks to be broken into manageable stages.

3.14 Limitations of Generative AI Prompting

Even well-designed prompts have limitations:

• AI may struggle to retain long conversational context

• Important details can be overlooked

• Outputs may drift from the original objective over time

This reinforces the need for human oversight and structured use.

4. Risks Arising from Poor Prompting

Poor prompting can introduce real organisational risks, including:

• Inappropriate use cases

• Inefficiency and wasted effort

• Inconsistent outputs across teams

• Incorrect or misleading information

• Biased outputs

• Diminishing trust in AI tools

• Loss of competitive advantage

  
  

These risks are often operational, not technical.

5. Mitigating Actions

Organisations can mitigate these risks through:

• Education and training in effective AI use

• A clear AI strategy aligned to business objectives

• Proportionate governance and guidance

• Disciplined use case discovery and prioritisation

This is where Orr Consulting supports leaders and managers — helping organisations move from ad hoc experimentation to controlled, value-led adoption.

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6. Final Thoughts

Your staff are already using AI.

Without guidance, this creates inconsistency, risk, and exposure. With the right structure, it becomes a powerful enabler of productivity and better decisions.

This leads directly into the next AI Insight - The Corporate Risk of Uncontrolled (Shadow) AI Usage - And How to Mitigate It.

7. Call to Action

If you would like support designing a practical, proportionate approach to AI adoption — including training, governance, and use case prioritisation — we would be pleased to help.

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