Summary of Key Points
In the era of AI, the core capability of a product manager is no longer merely to "understand the business" but to transform the vague rules in the minds of business professionals into standards that AI can execute. The test set is a direct manifestation of this ability. A test set is not just a simple "exam paper"; it defines clear criteria for determining whether an AI's output is "correct/incorrect/not good enough," which helps to stabilize the quality of AI-generated results. The article uses the example of a "content creation agent" to illustrate the value of a test set, its methodology, and how it has become a critical barrier for traditional product managers transitioning to AI roles.
Detailed Explanation
1. Why does the level of an AI product manager depend on the test set? Because AI output is **unstable**
Traditional software operates like cooking according to a fixed recipe: the same inputs always yield the same results. However, AI is like a beginner learning to cook; with the same ingredients, the taste may vary from day to day, or even change when using a different "model" (algorithm). At this point, it becomes essential to establish clear criteria for what constitutes good or bad quality. The test set serves as these standards for evaluating the output. If a product manager can only say that something is not good without specifying why (for example, too much salt or insufficient cooking time), AI will likely make the same mistake again. Therefore, the depth of the test set reflects the product manager's understanding of the business—whether they can turn vague assumptions into verifiable rules.
2. What exactly is a test set? In simple terms, it's the "grading criteria" for AI
A test set is not just a collection of questions; each question comes with specific **evaluation guidelines*. Each test case should address four aspects:
- What input was provided to the AI? (For example, asking the AI to write about how traditional product managers need to improve their business skills.)
- How should the AI handle the task? (For example, suggesting that the topic is too general and needs to be broken down into more specific scenarios.)
- What should the output include? (For example, identifying a target audience, a specific context, and a direction for improvement.)
- What constitutes a failure? (For example, avoiding vague statements like "understand user needs.")
For instance, if the AI generates an article with only empty, meaningless content, the test set can clearly determine that it has failed because it does not meet the requirements of having a specific context and a clear direction for improvement.
3. To create a good test set, three steps are necessary: turning vague assumptions into concrete standards
To convert business professionals' intuitive judgments into rules that AI can understand, product managers need to:
- Break down "good" into verifiable criteria: For example, when evaluating article quality, it's not enough to say it looks good; instead, the criteria should be based on data, clear structure, and resolution of contradictions.
- Quantify intuitive judgments: If business professionals suggest that a topic is too broad, product managers need to define it in a way that avoids ambiguity and conflicts with potential readers.
- Anticipate potential issues: Business experts can identify situations where errors are likely to occur (such as handling sensitive customer information); product managers should include these scenarios in the test set to help AI avoid them.
4. A good test set must address four types of scenarios: ensuring reliability and avoiding mistakes
A qualified test set should cover four types of situations:
- Typical cases: Can the AI handle normal tasks effectively? For example, can it determine whether a topic is worth addressing?
- Boundary cases: Can the AI make reasonable judgments in ambiguous situations? For example, for controversial topics like "AI replacing traditional product managers," can it warn of potential risks and suggest more specific areas of focus?
- Redline cases: What are the absolute no-go's? For example, if customer information is confidential, the AI must be programmed to remove sensitive data; if it cannot handle this, the topic should not be included.
- Historical bad cases: Can the AI avoid repeating past mistakes? For example, if previous articles were poorly written (e.g., with catchy headlines but empty content), these should be included in the test set so that the model is retested after each update.
5. The real barrier for traditional product managers transitioning to AI roles: not technology, but the ability to **refine standards**
Many people think that transitioning to an AI product manager requires technical expertise, but in fact, the technical barriers are decreasing (as AI tools can handle some technical issues). The real challenge is to transform the vague statements from business professionals into actionable rules for AI. For example, if a business professional says an article is ineffective, the product manager should break down the criticism into specific issues, such as a lack of a real context in the introduction or unmet promises in the headline. This is the essence of a test set and the key to the transition.
In conclusion
In the AI era, the core role of a product manager is not to "understand the business" but to document business rules in a way that AI can follow. The test set serves as this documentation, determining whether AI can truly help businesses solve their problems.
(End of article)