Prompt Engineering for Smarter AI Results

A strong AI output starts with clear instructions. If you are using AI tools to save time, improve reporting, or reduce errors, you need to know how to structure your prompts. That’s where effective prompt engineering can help.

Being precise and clear in your request is important when you are working in financial services. From writing client reports to queries around compliance, the way you phrase a request can mean the difference between a useful answer and a generic one.

We take a moment to break down what prompt engineering is, why it matters, and how to use it to your advantage for the best results.

What Is Prompt Engineering?

AWS describes “Prompt engineering [as] the process where you guide generative artificial intelligence (generative AI) solutions to generate desired outputs. Even though generative AI attempts to mimic humans, it requires detailed instructions to create high-quality and relevant output.” The goal is to reduce guesswork for AI and get closer to the result you want on the very first try.

Think of it as writing a structured task, rather than asking a question. That includes getting specific on tone, format, role, and the background information necessary to deliver an accurate response.

“Think of prompt engineering as programming with words.” (MIT Sloan)

Why Does Prompt Engineering Matter for Financial Advisers?

Precision is central to the work that financial advisers do. When AI tools are used for summaries, reports, or data analysis, unclear prompts can result in errors, delays, or even compliance issues.

Clear, detailed prompts lead to more accurate and reliable results. And when tools are used for regulated or client-facing work, there’s no room for miscommunication.

Furthermore, “different models may respond better to specific formats, such as natural language questions, direct commands, or structured inputs with specific fields. Understanding the model’s capabilities and preferred format is essential for crafting effective prompts.“

Five Elements That Make a Prompt Effective

Use these five principles to build a better prompt:

1. Define the task clearly
Instead of: “Explain pensions.”
Try: “Write a 150-word summary of how defined benefit pensions work for new UK employees.”

2. Set the role
Example: “You are an FCA-compliant paraplanner writing in plain English.”

3. Specify the format
Be clear about the output—table, list, paragraph, bullet points. (Atlassian)

4. Add context or examples
Reference real or sample inputs so the AI knows what kind of tone or structure you expect.

5. Iterate if needed
Don’t hesitate to reword your request. A small change in phrasing can lead to a big difference in output.

Use a Simple Structure to Frame Your Prompt

Atlassian‘s Ultimate Guide to Writing Effective AI Prompts shares that good prompts typically follow this format:

Persona – Who is making the request
Task – What you want the tool to do
Context – Any helpful background or details
Format – What kind of output you expect

Common Prompt Types That Work Well

OpenAI’s Best practices for prompt engineering include following a “zero-shot, few-shot, and fine-tune“ process until you receive your desired results:

Zero-shot – Ask for a task directly
Few-shot – Add examples to guide the model
Instructional – Use clear verbs like “List” or “Summarise”
Role-based – Set the perspective or expertise level
Chain-of-thought – Break down tasks step by step
Multi-turn – Build on earlier responses in a longer exchange

Each type works well in different use cases depending on complexity and tone.

Open AI’s best practices for Prompt Structure Examples

Basic:
“Write a product description.”

Improved:
“Write a 2–3 sentence product description in a friendly tone for a new oat-based skincare product aimed at 20–35 year olds.”

Best:
“You are a brand copywriter. Write a 2–3 sentence product description in a friendly tone for a new oat-based skincare product aimed at 20–35 year olds. Highlight its natural ingredients and cruelty-free sourcing.”

Why Being Specific Is Better Than Being Creative

Specific prompts help the AI narrow in on what you really want. According to Google Cloud and MIT Sloan, the more details you give about your audience, tone, and structure, the more accurate the output will be.

Prompt engineering is about being clear, and not clever. Remember to be specific!

Beyond Prompting: Focus on the Problem, Not Just the Input

One of the most important parts of using AI well is defining your problem clearly. If you don’t know what success looks like, the model will have a difficult time delivering it.

Before you write a prompt, ask: what’s the outcome I need?

Where to Learn More?

To sharpen your prompting, explore these practical guides and resources:

We built the Automwrite software with these principles in mind. Our AI-powered assistant is designed specifically for financial advisers, so you can ask questions, draft client letters, or generate reports with confidence. Explore how smart prompting paired with the right tool can save you time and reduce errors.