8 Proven AI Prompting Tips Recommended by Experts for ChatGPT, Gemini, Claude & More

Artificial Intelligence (AI) tools like ChatGPT, Google Gemini, Claude, and Perplexity AI are increasingly becoming indispensable digital assistants for professionals and students alike. With new AI chatbots and features emerging frequently, the one factor that remains universally crucial is the quality of user prompts. Prompts are the instructions or questions users give to an AI model, and mastering prompt engineering — even without a computer science degree — can dramatically improve productivity, creativity, and efficiency.

Understanding Prompts through a Framework

One widely recommended technique is the Persona-Task-Context-Format (PTCF) framework. This method structures prompts to give the AI a clear direction. “Persona” defines who the AI should pretend to be (e.g., a tour guide), “Task” explains what it must do (e.g., suggest places to eat), “Context” gives background (e.g., the user is in Delhi for three hours), and “Format” specifies how the answer should be structured (e.g., bullet points or a table). Using at least two or three of these elements leads to far better results.

Clarity is Key

Ambiguous prompts are one of the most common mistakes users make. Specific and well-defined prompts result in more accurate and relevant responses. For instance, instead of saying “Write a summary,” it’s more effective to say: “Summarize this article in 5 bullet points focusing on economic implications for the IT sector.” Experts recommend using action-oriented verbs like "translate," "rephrase," "summarise," "create," and "compare," while also including constraints such as tone, target audience, and length.

Refinement and Iteration

Prompting is not a one-time process. Treat it as an iterative conversation with the AI. If the first output isn’t ideal, refine the prompt by adding details or changing the format. You can ask the AI to convert a paragraph into a table or flashcards, or change the tone from formal to casual. This iterative back-and-forth not only improves the output but also teaches users what types of prompts generate optimal responses.

Use of Examples and Role-Play

Examples can guide AI to produce desired results. This technique, called few-shot prompting, involves including sample inputs and desired outputs. For example: “This is a sample tweet combining humor and statistics for climate awareness. Write five more in the same style.” This works especially well for writing tasks, categorization, and data formatting.

Assigning specific roles to the AI enhances the quality of responses. For example, asking it to act as a “career coach” results in more targeted advice. The tone and knowledge depth also shift based on the assigned persona. The clearer the persona, the sharper the results.

Reasoning and Data Context

Advanced models like ChatGPT and Gemini are capable of chain-of-thought reasoning. Asking them to “think step by step” helps with complex problem-solving in areas like coding, logic, and mathematics.

You can also upload your own files for personalized outputs — though it's important to exercise caution with sensitive data. This is particularly helpful when summarizing reports or referencing internal documents.

Practice, Save, and Optimize

Prompt engineering is a trial-and-error process. What works once may not always work again. Users should document effective prompts and reuse them for repetitive tasks like report writing, content generation, or customer support.

In conclusion, AI isn’t meant to replace human intelligence but to amplify it. Whether you're a writer, student, marketer, or researcher, developing better prompting skills can make working with AI more productive, enjoyable, and impactful.