5 Skills needed to become a prompt engineer  

AI is here. It’s changing how we work, think, and solve problems across every industry. 86% of CEOs expect AI to be embedded in their organisations this year.

Prompt engineering is the bridge between what you want and what AI delivers. It’s not just about using AI, it’s about speaking its language and getting results that matter.

Industry research highlights the rising importance of this skill as AI could add an additional $15.7 trillion to the global economy by 2030, with much of this potential unlocked through human-AI collaboration.

In our whitepaper, Workforce 2.0: AI Adoption and the Future of Jobs, we asked senior leaders across industries to identify the top three AI skills of the future, and they were:

• Prompt engineering
• Critical thinking
• Data engineering

Understanding what prompt engineering really involves is key to unlocking AI’s potential in the workplace.

In this article, we explore: 

• What is prompt engineering?
• Do you need to know how to code to become a prompt engineer?
• Top 5 skills needed to become a prompt engineer
• Prompt engineering tips to improve results
• The future of prompt engineering
• Ethical considerations in prompt engineering
• Conclusion
• Getting started with FDM

What is prompt engineering

Prompt engineering involves creating clear, specific instructions to help AI produce better, more consistent results. Professional prompt engineers work to understand how AI works and how to guide its behaviour to match what people want. If you’ve ever changed your wording to get a better answer from ChatGPT, you’ve already practised prompt engineering.

How you ask matters. Vague prompts get vague answers. Clear prompts unlock real insights.

For example, suppose you want an AI model to summarise an article. A poorly-crafted prompt might be: “Summarise this.” This vague instruction can lead to a generic or incomplete result. In contrast, a well-crafted prompt would be: “Summarise the following article in three concise bullet points focusing on the key arguments and main outcomes.”

FDM Skills Lab Coach, UK & EMEA, Nikola Ignjatovic, shares, “In an AI-driven workplace, prompt engineering is less about advanced techniques and more about communication maturity. The biggest gains come not from complexity, but from clarity, setting explicit goals, defining outputs, and giving precise instructions.”

In practice, prompt engineering combines language, logic, and problem-solving. It requires understanding how AI interprets instructions and refining your approach through iteration. For businesses, this skill is essential to ensure outputs align with organisational goals.

Do you need to know how to code to become a prompt engineer?

A common misconception is that prompt engineering requires extensive technical knowledge or programming expertise. In reality, while coding skills are helpful in some contexts, they are not essential for most roles.

Prompt engineering relies on communication, analytical thinking, and clear instruction structuring. The key is the ability to break down complex problems, provide clear context, and evaluate AI outputs effectively.

As Gangotri Bhatt, Director of Skills Lab, UK & EMEA, explains, “Prompt engineering isn’t really a standalone role. It’s a transitional skill that forces professionals to think clearly, communicate intent, and structure problems effectively, capabilities that apply across any role, technical or not.”

Understanding programming languages like Python can help integrate prompts into automated workflows, interact with AI APIs, and scale solutions across larger datasets. Similarly, knowledge of machine learning basics enables you to anticipate model behaviours, recognise biases, and optimise outputs effectively.

Top 5 skills needed to become a prompt engineer

Here are the five skills you need and how to build them:

1. Knowledge of large language models (LLMs)  

Understanding how LLMs function is fundamental. Prompt engineers must know how models interpret inputs, the patterns they use, and how context influences their outputs.

For example, a data analyst wants AI to identify trends in data:

• A vague prompt like “Analyse this data” might produce a generic summary with missing insights or a misinterpretation of key patterns.
• A clearer prompt would be: “Analyse this dataset of Q1 sales by region. Identify the top three performing products, highlight any positive trends, and suggest potential causes, summarising findings in a PowerPoint presentation.”

Familiarity with popular AI systems, their capabilities, and limitations allows professionals to write prompts strategically. Without this knowledge, prompt engineers may rely on trial and error, resulting in inconsistent or inaccurate outputs.

Recognising the nuances of each model, whether designed for conversation, summarisation, or coding, is crucial for effectiveness.

For example, Claude: Analyses data, summarising long documents, and producing structured responses.

Microsoft Copilot: Integrates into Word, Excel, and Teams, perfect for workplace productivity.

ChatGPT: Is great for brainstorming ideas and explaining complex topics.

2. Knowledge of prompting techniques  

Prompt engineering is all about how you interact with AI. Here’s what works:

• Zero-shot prompting – asking the model to perform a task without examples
• Few-shot prompting – providing examples to guide the model
• Chain-of-thought prompting – guiding the AI step-by-step

It’s not just about asking questions; it is about structuring instructions to achieve actionable, accurate, and contextually relevant results.

Nikola shares, “Prompt engineering is an iterative discipline, not a one-shot activity. Skilled practitioners expect to refine, test, and adjust prompts systematically.”

This includes:

• Evaluating outputs for accuracy, relevance, and alignment
• Adjusting constraints, wording, or examples
• Testing robustness across variations

Iterative prompting is now recognised as a core engineering workflow, particularly in production and analytical contexts.

Good prompt engineers don’t ask: “Did this work?”
They ask: “Under what conditions does this fail?”

However, Nikola highlights, “The real shift is moving beyond one-off prompts. High-performing professionals focus on reusable prompt patterns, templates, repeatable structures, and documented examples. This transforms prompt engineering from experimentation into scalable workflow automation.”

Melik Cinar, FDM Skills Lab Coach EMEA, believes “Prompt engineering is becoming less about finding the perfect phrase and more about knowing how to guide AI well — giving it the right context, setting clear boundaries, and shaping how it works within a real task or workflow. As AI becomes more embedded in everyday work, the role will feel less like ‘talking to a chatbot’ and more like designing reliable, useful systems that people can actually trust.”

3. Basic understanding of machine learning

Prompt engineers do not need to be machine learning experts, but they should understand the core principles that drive AI behaviour. This includes:

• Training datasets
• Bias and fairness
• Model evaluation
• General limitations of AI

Understanding why a model might misinterpret certain inputs or generate biased outputs enables prompt engineers to design prompts that minimise errors and ensure more reliable results.

71% of AI failures are caused by data or bias issues, highlighting the importance of this knowledge.

This understanding also helps professionals communicate more effectively with data science teams and bridge the gap between technical and non-technical stakeholders.

4. Programming skills  

Programming skills have become increasingly valuable, and languages like Python are commonly used to automate prompts, interact with APIs, and integrate AI outputs into broader workflows.

Even basic coding knowledge allows prompt engineers to scale solutions, analyse and test prompts. In a rapidly evolving field, this skill can provide a significant edge, especially in organisations embedding AI deeply into operations.

For example, instead of manually entering the same prompt multiple times, a prompt engineer with basic Python knowledge could automate the process by sending variations of a prompt to an AI API, collating the responses.

This transforms prompt engineering from a manual task into an efficient, repeatable workflow.

5. Data analysis and evaluation skills  

Analysing quality, identifying errors, and refining prompts are key responsibilities for consultants using AI within their organisation. For example, a data consultant should always identify errors and refine prompts when using AI to generate reports or insights.

Nikola says, “Early-career professionals should never treat AI output as final. Instead, they should build habits around fact-checking, asking what’s missing, and comparing multiple variations. This not only improves results but builds trust with teams and stakeholders.”

To make this process clearer, here is a simple checklist you can use:

• Clarity: Is the response clear and easy to understand?
• Relevance: Does it address your request?
• Accuracy: Are the facts verified?
• Completeness: Is anything missing?
• Consistency: Are there contradictions?
• Bias and Fairness: Is it balanced?
• Usability: Is it fit for purpose?

Another critical layer is domain understanding. AI amplifies what you already know; it doesn’t replace it. The strongest prompt engineers combine business context with AI capability, avoiding generic outputs and producing insights that actually matter.

Prompt engineering tips to improve results

The following strategies can help refine your prompts:

1. Be specific: Define the format, tone, and scope. Specific prompts produce better outputs than vague instructions.

Example: Instead of “Write an analysis,” try “Write a two-paragraph summary of the latest IT system audit, highlighting any security risks and recommended next steps in a professional tone.”

2. Assign roles: Ask AI to respond from a perspective, such as consultant, analyst, or strategist, to guide tone and style.

Example: “As a software engineer, explain the performance in this Python script and suggest optimisations.”

3. Provide examples: Including a reference for a desired output helps AI align responses more closely with expectations.

Example: “Use this previous incident report as a model to draft a similar report for last month’s network outage.”

4. Break complex tasks into steps: Guiding AI step-by-step allows refined control.

Example: “Step 1: Identify errors in the log file. Step 2: Categorise them by severity. Step 3: Suggest actions for critical issues.”

5. Iterate: First responses are rarely perfect; refine, test, and adjust prompts continuously.

Example: Generate an initial draft, then prompt: “Now summarise this into a concise technical brief suitable for a team meeting.”

The future of prompt engineering  

As AI becomes embedded across industries, it will shift from a niche skill to a core workplace capability.

Gangotri explains, “This shift is already happening. Prompt engineering is dissolving into everyday work, not because it’s becoming less important, but because it’s becoming invisible.”

Ethical considerations in prompt engineering

Prompt engineers will play a critical role in ensuring outputs are accurate, unbiased, and aligned with organisational values.

Nikola highlights, “Responsible AI use doesn’t come from trusting models less — it comes from designing the systems around them more carefully.

One of the biggest shifts organisations must make is treating prompts as governed assets, not disposable inputs. Prompts define how AI behaves, meaning they require ownership, review, and accountability, much like any other business-critical system. Without this, organisations risk “prompt sprawl”, where inconsistent or unmonitored prompts lead to unreliable and potentially harmful outputs.

Equally important is embedding ethical thinking directly into prompts. Rather than relying on policy documents alone, organisations are increasingly designing prompts that require AI to state assumptions, flag uncertainty, and avoid unsupported claims. This approach ensures responsibility is built into the interaction itself, not applied afterwards.

Human oversight also remains critical. In high-impact scenarios, AI should not act as the final decision-maker. Instead, prompts should clearly define when outputs are advisory, when human review is required, and where accountability sits. This reduces the risk of over-reliance on AI and reinforces trust in decision-making processes.”

Melik shares, “The most important thing organisations can do is remember that AI output should support human decision-making, not replace it. Responsible use comes from putting the right guardrails in place — clear rules, trusted information sources, regular testing, and human oversight — so people are not just accepting outputs at face value. In the end, ethical AI is really about accountability, transparency, and knowing when a human needs to step in.”

Nikola also says, “Ethical AI depends on how outputs are evaluated. Moving beyond instinct or ‘gut feel’, organisations are adopting structured review processes — assessing accuracy, bias, consistency, and risk. This treats ethics as something measurable and improvable, not abstract.”

Conclusion

Prompt engineering is now essential. As AI becomes more integrated into business operations, the ability to guide and collaborate with these systems will define professional effectiveness.

Developing knowledge of LLMs, mastering prompting techniques, and strengthening evaluation skills position professionals at the forefront of this shift.

Getting started with FDM 

At FDM, consultants receive training designed to build both technical expertise and professional skills, helping them launch successful careers in technology.

Through structured learning, mentorship and real-world project experience, consultants gain exposure to the tools, systems and environments used by global organisations.

Learn more about our opportunities, consultant roles and career pathways here.