Beginner's guide to prompting patterns for practical work

Beginner's guide to prompting patterns for practical work

Prompting patterns are repeatable ways of asking an AI to produce reliable outputs for day-to-day tasks. For someone new to prompts, patterns provide structure and reduce guesswork when you want specific results such as summaries, code snippets, checklists, or data extraction. This guide introduces a small set of approachable patterns, explains when to use each one, and shows how to iterate prompts so the AI becomes a dependable tool in your workflow.

Start by defining the job you want the model to do and the format you expect. A clear goal might be "summarise this meeting into action items" or "convert these bullet points into a short email". Always state the desired output format up front, such as "Give me a numbered list" or "Return JSON with keys title and body". Being explicit about format reduces follow-up edits and makes the results easier to process manually or automatically in downstream tools.

• Instruction pattern: direct command plus constraints, for example "Rewrite the paragraph in plain English, under 50 words".
• Role pattern: assign a role to guide tone and perspective, for example "You are a legal assistant. Draft a short clause about confidentiality".
• Stepwise pattern: ask for a plan first, then request execution, useful for complex tasks that need reasoning.
• Example pattern: provide one or two examples of the desired output to show format and style.
• Refinement pattern: request an initial draft, then specify edits such as "make more formal" or "shorten by 25 percent".

One practical workflow is: define the goal, choose one pattern above, give a concrete format, provide context and any examples, and then iterate. For instance, to create a project brief, start with "You are a product manager. Produce a brief with Objective, Scope and Timeline sections." Add a short background paragraph, then ask for the brief. If the first result misses something, use the refinement pattern to include missing details or change tone. Iteration is not a failure but an expected part of the process.

When prompting for structured outputs that will be parsed, be explicit about syntax and edge cases. Ask for output in JSON or CSV when you plan to feed the result into a script, and describe how to handle missing values. For freeform tasks, combine the role pattern with explicit constraints such as length limits and target audience. If precise wording matters, include an example sentence and ask the model to match its style. These small clarifications save time and improve accuracy.

Common beginner mistakes include being vague about format, giving too much unstructured context, and not checking the first result for minor errors you could easily correct with a follow-up prompt. Another pitfall is expecting perfect outputs on complex tasks in a single prompt; instead aim for incremental improvements. Keep prompts concise but complete, and prefer a series of short, targeted prompts over one very long and unfocused one.

Here is a simple prompt template you can adapt: role description, task instruction, context, desired format, example, and revision instruction. For example: "You are a marketing analyst. Summarise the following report into three key insights, each two sentences. Use plain language and bulleted format. Example: Insight: Sales rose because of X. If a key data point is missing, note it explicitly." Practising with small prompts like this builds pattern recognition and speeds up your work. For more builds and experiments, visit my main RC projects page.

To keep learning, practise these patterns on routine tasks and review outcomes critically so you can refine your approach over time. If you want more structured examples and posts from this site, see the collection of AI and automation articles on the blog for related exercises and templates at Build & Automate's AI & Automation tag.