The CATS Framework: How European Professionals Are Finally Getting Useful Answers From AI

Why Prompting Is the EU's New Digital Literacy

Europe's AI adoption curve is steepening sharply. The EU AI Act, which entered into force in August 2024, has prompted organisations across the bloc to think seriously about how they deploy and interact with AI systems. But regulation alone does not make workforces fluent. That requires training, and at the heart of effective AI training is prompt craft.

Luc Julia, Chief Scientific Officer at Renault Group and one of France's most prominent AI voices, has argued consistently that AI tools are only as useful as the instructions they receive. Speaking at the VivaTech conference in Paris, he emphasised that human judgement and precise direction remain irreplaceable, regardless of how capable the underlying model becomes. His position reflects a growing consensus among European AI practitioners: the human side of the human-machine interface needs urgent investment.

At the policy level, the European Commission's AI Office, established under the AI Act framework, has flagged AI literacy as a priority for member states. The Commission's own guidelines for public sector workers stress iterative, context-rich interaction with AI tools rather than single-shot querying. In plain terms, officials are being told to treat AI like a knowledgeable colleague, not a search engine.

Prompting is becoming the new typing: a basic skill that quietly underpins productivity. The challenge is moving beyond the frustration of asking smart questions and receiving flat, generic answers.

The CATS Framework: Context, Angle, Task, Style

One structured approach gaining real traction in enterprise training programmes across the UK and the EU is CATS: Context, Angle, Task and Style. It is not a magic formula, but it is a repeatable process, and repeatability is precisely what most ad hoc prompting lacks.

Context means setting the scene with genuine precision. "Write a proposal" is a weak prompt. "I am a programme director at a UK university crafting a funding application for a digital skills initiative targeting mature learners; here are the funder's criteria" is a strong one. Upload relevant documents. Spell out constraints. Make the situation specific enough that the model has no room to default to generality.

Angle exploits one of AI's most underused strengths: perspective-taking. Asking the model to adopt a role sharpens its output considerably. "Act as a sceptical procurement officer reviewing this supplier pitch" or "Respond as a senior HR business partner advising a junior manager on a difficult conversation" produces far more targeted material than an unframed request.

Task demands explicitness. Instead of "help with my presentation," specify: "Suggest three ways to make the opening slide more compelling for an audience of mid-sized manufacturing company executives attending a digital transformation briefing." The more granular the task, the less the model has to guess.

Style closes the loop. AI is a genuine chameleon when directed, but entirely unpredictable when not. Do you want a formal board-level report, a punchy executive summary, five bullet points, or a conversational briefing note? Say so explicitly. Specify the register: technical, persuasive, plain-language, legally cautious.

A fifth element, iteration, sits beneath all four. Treat the exchange as a genuine back-and-forth rather than a vending machine transaction. Push back on weak outputs. Ask follow-up questions. Request the model to revisit a specific paragraph with a different tone. The interaction improves dramatically when the user commits to the dialogue.

Context Engineering: The Skill Beyond the Single Prompt

Experienced users are moving beyond individual prompt construction into what researchers are beginning to call "context engineering": the deliberate management of everything surrounding the prompt itself. This includes chat history, uploaded reference documents, worked examples, and the cumulative logic that builds across a long session.

Researchers at ETH Zurich, whose AI Centre is one of Europe's leading applied AI research hubs, have explored how large language models perform very differently depending on the density and relevance of contextual information provided. Their work reinforces what practised users already know intuitively: the richer and more structured the surrounding context, the more precise and useful the model's outputs become.

Professional users in high-output environments, legal firms, consultancies, publishing houses, are increasingly maintaining personal prompt libraries: curated collections of formulations that have proven reliable for recurring tasks. This is not a workaround; it is professional infrastructure, as legitimate as a well-organised template folder or a house style guide.

A Practical Comparison: Prompt Quality and Output

• Basic prompt ("Help me write an email"): generic output requiring heavy editing; useful mainly for initial brainstorming.
• Structured prompt ("Write a professional follow-up email after a client meeting about software procurement"): relevant output that still needs personalisation; good for template creation.
• Precision prompt using CATS (full context, defined role, specific task, stated style requirements): production-ready content needing only light personalisation and a factual check.

The difference in output quality between the first and third categories is not marginal. For organisations processing large volumes of written communication, marketing copy, policy documents or training materials, it translates directly into measurable hours saved per employee per week.

The Human-First Principle

Even the most precisely engineered prompt will not rescue you from over-trusting the machine. AI language models sound authoritative and fluent, but they do not reason. They predict. Hallucinations, factual errors and confident inaccuracies are not edge cases; they are inherent features of probabilistic systems operating without genuine comprehension.

Yoshua Bengio, Turing Award winner and founder of Mila in Montreal, has long cautioned European policymakers and practitioners alike that AI systems require active human oversight rather than passive acceptance of outputs. His arguments, widely cited in EU AI governance discussions, apply equally to everyday prompting practice. Every AI output is a draft, not a deliverable, until a human has verified it.

The goal of better prompting is not to eliminate human input. It is to elevate human capability by reducing the cognitive overhead of producing a first draft, structuring an argument, or exploring a problem space. AI accelerates; it does not replace. That distinction matters enormously in professional contexts where accuracy and accountability are non-negotiable.

Common Questions on Prompt Craft

How long should a good prompt be? Research suggests that effective ChatGPT prompts average around 60 words, compared with the 3 to 4 words typical of a web search. Length matters less than specificity. A focused 40-word prompt will consistently outperform a rambling 150-word one.

Do prompting skills transfer between tools? Yes. The core principles of context-setting, specificity and iteration apply across ChatGPT, Claude, Gemini and Copilot. Individual tools have distinct features, but the human skill set is largely portable.

How do you know if you are improving? Track how often you can use an AI output with minimal editing. Beginners typically rewrite most of what the model produces. Skilled prompt writers regularly receive usable first drafts that need only light personalisation. That ratio shifts noticeably with deliberate practice over two to four weeks.