Have you ever sent a message asking for help on a project, only to realize you didn’t include the information the other person needed — like deadlines, goals, or the audience? The same principle applies to working with AI language models: the more precise and detailed your instructions, the more reliably the AI delivers useful results.

LLMs (Large Language Models) depend entirely on your input to generate relevant and actionable answers. Prompts that lack specificity or structure can lead to responses that miss the point or require repeated corrections. Consider this realistic example:

• Unfocused prompt: “Can you help me write something for our team update?”
  • The AI may respond with a general template or an update that doesn’t address your needs.
• Focused prompt: “Write a one-paragraph update about last week’s product launch progress for a mixed technical/non-technical audience. Include major milestones and one metric.”
  • This gives enough context and direction for a highly relevant output.

Let's consider the best prompting practices.

Clarity and explicit requirements are key. Avoid ambiguity about what you want. The more specific your language, the more accurate the results.

• Vague prompt: “Summarize this report.”
• Specific prompt: “Summarize this 10-page quarterly marketing report in 3 bullet points, focusing on campaign effectiveness and sales outcomes, and write so that non-marketing staff can understand it.”

Using Strict Language:
Being strict helps the model understand what’s non-negotiable. For example, using words like must, capitalizing requirements (for example, “YOUR RESPONSE MUST INCLUDE A LIST OF AT LEAST 5 ITEMS”), or clearly specifying boundaries can significantly improve compliance. Compare:

• Loose: “You should include some action items.”
• Strict: “You MUST include at least three specific action items for the development team in bullet form.”

Using stronger, more directive language makes your must-haves clearer.

AI models often align their output closely to input examples — this is called few-shot prompting. When you supply an example of the kind of answer or format you expect, the model is much more likely to mirror it.

• Without example: “Write a project status update.”
• With example:
  “Write a project status update in this style:
  • [Project Name]: [Status: On Track/Delayed]
  • [Key Accomplishments]:
  • [Next Steps]:
    Use short, clear bullet points.”

This technique works especially well when requesting complex structures (like tables, formatted emails, or step-by-step instructions) or using a particular tone or level of detail.

Many tasks asked of LLMs combine several steps — for example, “Draft a technical proposal for a new product” requires outlining, technical writing, and summarizing. If you try to get the entire answer in one go, the model may miss important components or take shortcuts.

Chain of Thought Reasoning:
Modern LLMs are trained to simulate “chain of thought” — where reasoning steps are made explicit. Yet, breaking complex prompts into bite-sized parts or requesting stepwise answers still improves clarity and performance.

• All-in-one prompt: “Help me design and document a new onboarding process.”
• Stepwise prompt: “First, list the key stages in an onboarding process for software engineers. Then, for each stage, draft a short description and expected outcomes. Present your answer as a table.”

Breaking prompts into stages makes it easier for the AI to follow your desired process, and helps you catch issues or adjust instructions at each step.

• The more precise and specific your prompt, the better the AI’s output.
• Giving examples shows the AI the format and tone you want.
• Breaking tasks into stepwise or logical parts helps the AI reason and deliver each requirement.
• Treat prompting as an interactive process — iterate and refine for the best results.

Ready to practice? Let’s sharpen your prompting skills!