Better prompts start before the prompt

AI can accelerate marketing technology development and enable more people to build software. But generating code quickly doesn’t guarantee that the result meets business needs.

Getting useful output starts with defining requirements before writing a prompt. Established development practices, including user stories and acceptance criteria, can help marketers give AI better direction, reduce wasted effort, and improve results.

Define what you need before prompting AI

Defining project requirements takes time and effort. Even the person who conceived the project may need to refine the vision as others contribute their perspectives.

AI can generate output in minutes, but that speed is wasted if the results don’t meet expectations. Repeated prompting and revisions can consume time and AI credits without bringing the project closer to its goals.

Established development practices can help teams define expectations upfront and reduce unnecessary iterations.

Development practices that improve AI output

Many existing artifacts help define intention. AI also speeds up their creation. These artifacts include:

• Business requirements documents (BRDs).
• User stories. 
• Acceptance criteria.
• Prompt frameworks. 
• Prompt logs.

Most tasks won’t require all of these. Changing the order of fields on a form, for instance, doesn’t require a formal BRD. A thoughtful user story paired with clear acceptance criteria may suffice.

Business requirements documents: Keep the big picture in focus

BRDs are important artifacts that provide a detailed business vision for large initiatives. They require stakeholders to state the overall goal and the metrics that determine success.

These documents should take a high-level business perspective. As project participants execute the vision, they’ll confront countless questions no one can fully anticipate. A clear business vision in a BRD helps a programmer, graphic designer, or vibe-coding marketer make decisions that align with it.

BRDs are useful when breaking up a large initiative into assignments for various teams.

User stories: Break projects into manageable tasks

User stories are lightweight statements developed by software engineer Kent Beck. They define the who, what, and why of a project or initiative.

User stories empower programmers to make minute decisions that align with a project’s end goal. In Scrum, the business stakeholders are responsible for providing a clear vision while the development team is accountable for determining how to achieve it. 

A user story is a simple statement that follows this format:

• “As an [actor], I want [a certain functionality] so that [reason].”

This isn’t sufficient to define the vision for migrating to a different website platform, but user stories suit many associated tasks. For example:

• “As a customer, I want autofill functionality in the address field so that I don’t need to type my entire address into the form.”

In Agile frameworks like Scrum and Kanban, user stories help break larger projects into manageable tasks. Other layers, such as epics, typically sit between project-level requirements and individual user stories.

In vibe coding, for example, using a single prompt to develop an entire website makes it difficult to ensure that all requirements are met. User stories help divide the work into smaller, more manageable tasks.

Acceptance criteria: Specify what success looks like

Acceptance criteria clarify user stories. They should not dictate how to accomplish the goal, but they help specify what the end user expects.

For instance, acceptance criteria for the address autofill user story could include:

• Upon user selection, populate the “Street Address,” “City,” “State,” and “ZIP Code” fields.
• Allow freeform input in the “Street Address 2” field so that an individual can provide a specific unit designation (e.g., “No. 2,” “Unit 3B,” or “Apartment 108”).
• Add a note stating, “Autofill ensures that the address matches postal system standards to ensure accurate delivery.”
• Provide an option for addresses outside the United States that don’t use autofill.

Including acceptance criteria in a prompt ensures it covers the necessary details.

Prompt frameworks: Make every instruction count

Prompt frameworks help you structure instructions before generating AI output. Like user stories, they encourage you to define essential details upfront rather than relying on repeated revisions.

For instance, the TAG framework focuses on a task, audience, and guardrails. Defining these as three essential elements helps fine-tune the natural language for a prompt.

Prompt logs: Build on what works

Prompt logs catalog patterns that yield desired output and codify patterns to avoid. Continually reinventing the wheel wastes time and causes frustration.

Prompt logs can also include wording necessary to produce secure, performant, and sustainable code. IT security and reliability engineers can provide phrases or sentences for vibe coders to incorporate into their prompts.

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How AI changes the way marketers define requirements

As AI lets marketers and other business stakeholders take a more direct role in generating code, they also need to take greater responsibility for defining requirements. Tasks like developing business requirements documents (BRDs) and user stories are no longer limited to business analysts, product managers, and IT professionals.

AI can help with this work. Instead of writing a 15-page BRD from scratch, marketers can use AI to generate a draft. They can also ask AI to pose clarifying questions before generating code or other complex output. These initial checks help identify missing requirements and prevent wasted effort.

Marketers still need to guide the process and evaluate the results. That means taking on more of an editor’s role — identifying gaps, clarifying requirements, and ensuring the output aligns with the project’s goals.

Faster AI development demands faster requirements gathering

As AI accelerates development, requirements gathering needs to keep pace. Without clear direction, faster code generation can produce systems that fall short of business needs or introduce security and performance problems.

User stories, acceptance criteria, and other established development practices help marketers define expectations before generating code. AI can help speed up that work, but marketers remain responsible for ensuring the results meet their goals.