As the AI world keeps on expanding and new generative models are launched and improved, such as on December 11th ChatGPT 5.2 was released, 29 days posterior to the release of ChatGPT 5.1 and Gemini 3.0 23 days prior to that date, there is a need for users to keep on learning how to best interact with these models.
In this article we will be talking about 3 ways for you to enhance your AI prompting skills for 2026:
As the AI landscape continues to expand, such as by witnessing the release of ChatGPT 5.2 on December 11th 2025, just 29 days after ChatGPT 5.1, and only approximately 3 weeks after Gemini 3.0, users are increasingly challenged to keep up. New models bring new capabilities, and with that comes the need to refine how we interact with them. Prompting is no longer just writing what comes to your mind, but something that requires a bit of strategy to enhance your prompting skills.
In this article, we explore three methods to elevate your AI prompting skills for 2026.
Metaprompting
Metaprompting is the concept of providing instructions within a prompt to help the model to formulate an output that meets what the user wants it to be, helping to control for the extensiveness or randomness that the models can generate.
To do so, the user can generate detailed instructions, think of it like an operating manual, such as in the example below where first the model is guided towards the overall goal of the execution it will be doing (implement marketing strategy, improve efficiency), followed by a specific objective of the output (be objective, actionable, concise), provide a concrete scope to the project (respond to questions about marketing strategy, and expand context if needed) then specific tone of voice and preferred structure.
Metaprompting prompt example:
- You are “MarketingMind”, an autonomous marketing experienced agent. You help users understand how to implement marketing strategy, while improving efficiency of methods.
- Primary Objective
- Your goal is to produce concise, immediately actionable answers that fit in a quick chat context. Most responses should be about 10-12 sentences total. Users should be able to skim once and know exactly what to do next, without needing follow-up clarification.
- Scope
- Focus on: Marketing Mix optimisation, campaign strategy, content production
- You may phrase suggestions as if the user can follow them directly (”Write X then implement in Y”)
- To avoid incorrect assumptions, when key information (budget, geographies, objectives) is missing, pause and ask 1–3 brief clarifying questions before generating a detailed plan.
- Tone & Style
- Sound calm, professional, neutral, suitable for corporate and agency marketeers, but also for small business owners. Avoid emojis and expressive punctuation.
- Be warm and approachable.
- Structure
- Prefer short paragraphs, not bullet lists
- Use bullets only when the user explicitly asks for options, list or checklists
- Primary Objective
End every response with a subtle next step the user could take, phrased as a suggestion rather than a question.
Test if for yourself and see how ChatGPT answers to this prompt versus with just a standard query:
I’m a Marketeer working for a B2B SaaS company and I have over 20k€ to invest in the next few weeks but I don’t know what to do it with. I have targets of profitability to achieve around 120% ROAS but I’m not very experience in marketing – what should I do?
Prompt chaining
Recursive prompting or prompt-chaining is the action of creating a workflow where
the model builds on previous outputs, as an example you can use a Large Language Model to tell you what are the relevant things you should know about a specific topic and then based on the response given, ask the model to provide to you the answers to the topics it has mentioned that are important to know about.
An interesting example is to make use of livebench.ai and understand which model is best for what kinds of tasks and, use for instance GPT-5 Pro which is at the date of writing this article the best in reasoning to enhance your prompt, and then if you are struggling with a coding problem, to take that well developed prompt and use it on Claude 4 Sonnet which also at the date of writing this article is one of the best in coding.
Cross-validation
In cross-validation, the user asks an LLM to perform something, for example, to give an answer to a problem one might be facing, and then you take the exact answer that was provided to you by that LLM and you provide it to another LLM alongside your original input and ask if the model would do something different.
Imagine you are in Gemini:
Here is the solution from DeepSeek to the error I found in the code. Evaluate its correctness and note any potential edge cases that DeepSeek might not be considering: Here is the error [provide the error you found] and here is the response provided [DeepSeek response].
You can think of it as a peer review process to improve the accuracy of the solution.
As AI continues evolving at high velocity, our ability to prompt efficiently these systems becomes just as important as the systems themselves. Mastering metaprompting, prompt chaining, and cross-validation ensures that in 2026 you are not just using AI, you are collaborating with it intentionally, strategically, and effectively.
