From Command to Context
Many of us treat large language models (LLMs) like a search engine or a digital vending machine. We type in a short command—"write a marketing email," "summarize this topic"—and expect a perfect result. When the output is bland or misses the point, we
tweak a word or two and try again. This approach, however, misses the fundamental power of these tools. The quality of an AI's output is directly proportional to the quality of the input. Instead of a one-line command, think of your prompt as the start of a conversation with a new, incredibly knowledgeable, but context-blind collaborator. The single most effective way to improve results is to provide the context that already exists in your head or your documents. This is the essence of moving from just prompting to what is sometimes called context engineering. You are not just giving an order; you are setting the stage for success.
Show, Don't Just Tell, with Examples
One of the most powerful forms of "work already done" is an example of what you consider a successful outcome. This technique is known in AI circles as few-shot prompting. Instead of just describing what you want, you show the model. For instance, instead of asking it to "write a tweet about our new software," you would provide a few examples of your brand's existing tweets that you like. Your prompt might look something like this: "Here are three tweets from our company. Notice the professional but conversational tone. Now, write a new tweet about our new analytics tool, focusing on how it saves time for busy professionals." By providing precedents, you give the model a clear style guide, a target to aim for that is far more specific than any adjective you could use. This dramatically reduces the chances of getting a generic response and aligns the output with your specific needs.
Leverage Your Drafts and Outlines
You don't need to start with a blank page. If you have already done some thinking, share it. A half-finished document, a bullet-point outline, or even a messy collection of notes are all valuable assets to include in your prompt. This is perhaps the most literal interpretation of the headline: you are showing the AI work you have already done. For example, you could paste in an outline for a report and ask the AI to flesh out the first section, or provide a rough draft of a client email and ask it to refine the tone to be more confident and concise. This transforms the AI from a pure generator into a powerful editing partner. It focuses its capabilities on a specific task—improving or expanding upon your existing work—rather than guessing at your intentions from scratch.
Define the Persona, Format, and Goal
The strategic thinking you do before writing is also a form of "work." This includes defining the audience, the desired tone of voice, and the format of the final output. Share this with the AI. A great prompt often includes a role for the model to play. For example, "Act as a senior marketing strategist writing for an audience of tech CEOs." This is significantly more effective than simply asking for marketing copy. You should also be explicit about the format. Do you need a bulleted list, a multi-paragraph summary, or a table? Specify it. Finally, state the goal. What is this text supposed to achieve? A prompt that includes persona, format, and goal gives the model clear guardrails, ensuring the output is not just well-written, but also fit for purpose.
Embrace the Iterative Process
The first response you get from a model is rarely the final one. Thinking of prompting as a one-and-done interaction is a common mistake. The real work begins after the first generation. This is known as iterative prompting. Treat the interaction as a dialogue where each response helps you refine your next question. If the AI produces a summary that is too long, don't just start over. Reply with: "That's a good start, but can you condense it to three key bullet points?" If the tone is wrong, say: "Make it more formal." This process of refining prompts based on feedback is a structured way to guide the model toward the perfect output. Each step in the conversation adds more context, building on the work you are doing together and progressively improving the result.
