Generative AI is only as powerful as the prompts you give it. By mastering a handful of proven prompting techniques, leaders can improve accuracy, unlock creativity, and turn AI into a more reliable partner across functions and industries.
Understanding how to use prompting effectively can be a major unlock in your AI journey. A handful of proven prompting methods can dramatically change how you work—across marketing, operations, HR, finance, and more.
A useful resource here is The Prompt Report: A Systematic Survey of Prompting Techniques, a 75-page paper that classifies and evaluates prompting approaches. It’s substantial, but it’s one of the better roll-ups available for leaders who want a structured view of prompting.
What Is Prompting?
A prompt is an input given to a generative AI model (ChatGPT, Claude, Gemini, etc.) designed to elicit a specific output. Prompts can be text, images, or multimodal combinations. How you craft them has a direct impact on the quality of the model’s response.
Put simply: the effectiveness of AI often comes down to how you ask.
Six Prompting Techniques to Know
1. Zero-Shot Prompting
No examples are provided—the model applies general knowledge to the task.
Prompt: “Write a professional email introducing a new service offering to potential clients.”
2. Few-Shot Prompting
Several examples are embedded in the prompt to guide the model.
Prompt: “Example 1: ‘Our platform reduces processing time by 40%.’ Example 2: ‘Our platform improves compliance reporting.’ Now, generate another benefit statement.”
3. Chain-of-Thought (CoT) Prompting
Encourages step-by-step reasoning before the answer.
Prompt: “Calculate the ROI of a project with an upfront cost of $500,000, annual savings of $200,000, and a 5-year horizon. Think step by step.”
4. Ensembling
Use multiple prompts for the same task and aggregate responses.
Example: Generating three strategic plans—one optimized for cost, one for speed, one for customer impact—then combining them into a single robust plan.
5. Self-Criticism
Ask the model to critique its own output, then regenerate.
Prompt: “Draft a response to a client asking for a discount, then critique your answer for tone, professionalism, and persuasiveness.”
6. Decomposition
Break complex problems into smaller steps.
Prompt: “Outline the process for onboarding a new employee. Then expand each step into detailed actions and best practices.”
Beyond Prompt Engineering: Answer Engineering
Prompt engineering is about asking the right way. Answer engineering is about ensuring you get back responses that are precise, actionable, and usable.
For example, a customer service leader might prompt an AI for troubleshooting steps. Prompt engineering ensures the model interprets the request correctly. Answer engineering ensures the response is concise, sequenced, and practical—something that can be executed in real time by the team.
Practice, Iterate, Improve
Prompting is still more art than science. Techniques matter, but so does practice. Build a prompt library. Test prompts across different models. Ask the models themselves to refine your prompts.
The more deliberate you are, the more value you’ll extract. And in a world where generative AI is evolving quickly, the organizations that master prompting will be the ones that turn novelty into advantage.
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