AI often disappoints not because its models are always poor, but because we give it tasks that are too large in one breath. For example, "Create a complete marketing strategy for my business, including audience research, content calendar, budget, and captions." Requests like this seem practical, but they actually mix many tasks with different success criteria.
For simple tasks, one prompt may be sufficient. However, for work that requires judgment, data, or several decisions, a safer approach is to create a step-by-step workflow: ask AI to understand the problem, outline options, develop results, and then audit its own answers.
The official OpenAI guidelines also suggest breaking complex tasks into smaller parts and working on them iteratively—meaning we review the initial results and then refine the instructions based on that output. OpenAI's guide on prompt creation explains this approach as a way to make interactions with the model more directed.
Why a single long prompt often results in mediocre answers?
When many goals are included at once, AI has to guess their priorities. Should it pursue completeness, speed, style, accuracy, or as many ideas as possible? If all are considered important, the result often becomes lengthy but shallow.
Another issue is that we haven't had the chance to correct initial assumptions. If AI misunderstands the target audience in the first stage, that mistake can carry through the entire article, work plan, or subsequent analysis.
A step-by-step workflow works like creating a house blueprint. We don't immediately ask the builder to put on the roof before knowing the land size and layout. The same goes for AI: ensure the direction and foundational materials are correct before asking for the final result.
A four-step workflow that can be used for various tasks
1. Ask AI to map the problem first
In the first stage, do not directly ask for the final result. Ask AI to summarize the goals, identify missing information, and highlight parts that need decisions.
"I want to create an AI usage guide for the customer service team. Before providing solutions, help map out: the main objectives, types of users, missing information, risks to consider, and three decisions I need to make first. Do not write the final guide."
This stage is useful because AI is used as a thinking tool, not just a text production machine. You can also see if the model understands the issue correctly. If the direction is wrong, correcting it now is cheaper than fixing a lengthy final result.
2. Ask for several options with clear reasoning
Once the problem is mapped, ask AI to outline options. Do not just ask for the "best solution," as the term best always depends on goals and constraints.
"Based on the context above, create three approaches. For each approach, explain the benefits, drawbacks, difficulty level, costs or resources that may be needed, and the conditions under which that approach is most suitable. Do not choose one option yet."
This way, AI helps clarify trade-offs—i.e., the exchange between one advantage and another disadvantage. The most comprehensive option is not necessarily the most realistic. For small teams, a simple and easy-to-maintain solution is often more valuable than a system that appears sophisticated.
3. Develop one option into a concrete output
Only after choosing a direction, ask AI to create a more concrete output. Include the chosen option, audience, format, length limits, and success criteria.
"Use the second approach. Create a workflow design for a team of five. Format it as numbered steps. Each step should have a goal, required inputs, expected outputs, and who is responsible. Use practical language, not unnecessary technical jargon."
Note that this prompt no longer asks AI to solve the problem from scratch. The context is now narrower, increasing the chances of relevant output.
4. Ask for an audit, not just a revision
The final stage is often overlooked. We ask AI to refine the language style, but do not request a check on assumptions and content weaknesses. However, such checks are more useful than merely making sentences sound neat.
"Audit the design above. Look for unclear parts, unproven assumptions, potentially failing steps, information that needs verification, and instructions that team members might misunderstand. Present the audit results in a table with columns: findings, impact, and suggested improvements."
This audit does not guarantee that all errors will be found. AI can still miss issues or provide overly general critiques. However, this format forces us to view the results from a different perspective before use.
Examples of application for everyday tasks
For instance, if you want to write a blog article about account security. Instead of directly writing the article, use the following sequence:
- Ask AI to identify the main questions of readers and common misconceptions.
- Request three article structures with varying depths.
- Choose one structure, then ask for the development of each section with concrete examples.
- Request an audit of claims that need verification, risky suggestions, and overly general sections.
For office work, the same pattern can be used when creating proposals, SOPs, meeting summaries, project plans, or customer feedback analyses. The only difference is in the audit criteria. Proposals need to be checked for cost and benefit assumptions. Meeting summaries need to be checked for decisions and responsibilities. Customer analyses need to be checked for biases and missing data.
When is this workflow unnecessary?
Do not complicate simple processes. If you only want to translate a paragraph, tidy up a shopping list, or find a few alternative titles, a direct prompt is usually sufficient.
Use the step-by-step workflow when the task has at least one of the following characteristics:
- the results will be used to make decisions;
- there are multiple goals that need to be balanced;
- errors could incur costs, confusion, or risks;
- you are not sure if the problem has been correctly formulated;
- the final result needs to follow a specific format or standard.
What you can do now
Choose one task that often makes you go back and forth asking AI for revisions. Break it down into four small conversations: map the problem, compare options, develop results, then audit.
Save prompts that have proven helpful, but do not treat them as a mantra that always works. Context, input quality, and your needs can change. OpenAI also emphasizes the importance of reviewing initial answers and refining prompts iteratively, rather than chasing a single arrangement of words deemed perfect. OpenAI's prompt engineering best practices guide refers to this iterative process as an important part of prompt engineering.
In essence, AI does not always need to be commanded to produce a final answer immediately. Often, more usable results emerge when AI is guided through a structured thinking process—while humans retain the final decision, fact-checking, and assessing whether the results truly meet the needs.
Sources & further reading
– Rio Yotto @rioyotto