Anthropic’s new generative AI, Fable5, is available again and is currently being trialed worldwide. I have been taking on new challenges myself, but I often feel that even with ultra-high-performance generative AI, things don't go well if the user's prompts are vague. Rework occurs every single time, and I've constantly wondered, "Isn't there a better way to do this?" Recently, Anthropic engineer Thariq Shihipar shared a brilliant idea (1). This time, I would like to introduce the "Blind Spot Pass"—a method that helps us identify our blind spots in advance—and actually try using it.

1. Reduce the Unknowns

I believe the core of Thariq Shihipar's argument is the "unknown." When starting a project, we don't understand everything from the outset, and there are many unknowns. He categorizes our knowledge as follows:

Known Knowns: This is essentially what is in my prompt. What do I tell the agent that I want?

Known Unknowns: What haven't I figured out yet, but I’m aware that I haven’t?
Unknown Knowns: What's so obvious I’d never write it down, but would recognize it if I saw it?
Unknown Unknowns: What haven't I considered at all? What knowledge am I not aware of? Do I know how good something can be?

No matter how high-performing Fable5 is, if we—the ones giving the instructions—don't clearly understand what we want to do, we won't be able to push its capabilities to the limit. Therefore, before starting a project, we need to transition from a state of "many unknowns" on the left to a state of "few unknowns" on the right. He outlines a detailed process for this, but I would like to focus specifically on the "Blind Spot Pass," which reduces the "Unknown Unknowns."

2. How to Strategically Use a Customer Churn Prediction Model

Currently, I am developing a "customer churn prediction model" combining machine learning and generative AI, but I am pondering how best to apply it to actual business scenarios. I have thought about various approaches, but that doesn't mean there are no blind spots. So, I would like to immediately apply the "Blind Spot Pass" to Fable5 and uncover these blind spots. The actual method just requires using the following prompt. It's very simple!

“I am looking to develop a new customer churn prediction model to improve my business, but I have no prior knowledge of customer churn prediction or how to practically apply it in a business setting. Please conduct a 'Blind Spot Pass' to identify the relevant 'unknown unknowns' and help me write a better prompt.”

Within minutes, I received the following response. It's amazing!

With blind spots identified in such detail, I can proactively grasp potential stumbling blocks before actually writing the prompts, allowing me to write them much more clearly and effectively. Furthermore, when feedback comes from Fable5, I will be able to respond more accurately. Because the unknowns have been reduced, I can move forward with the project with confidence.

3. The "Blind Spot Pass" is Also Effective for Formulating Management Strategies

Because Thariq Shihipar is an engineer, his discussion centered around coding, but reducing unknowns is crucial in a variety of fields. Especially in management strategy formulation, where uncertainty is high, eliminating blind spots as early as possible is extremely important to prevent failures caused by "unforeseen circumstances." The development of the customer churn prediction model mentioned above shouldn't be viewed merely as a coding task, but rather as an action responding to the major management challenge of "increasing customer retention," making it a significant pillar of management strategy. If we consider management strategy after grasping the 11 points raised earlier, we will undoubtedly be able to build a more effective and refined strategy. I want to actively keep using this. The "Blind Spot Pass" serves as an incredibly reassuring partner, especially when taking on new challenges in uncharted territories.

What do you think? Just by tweaking your prompts slightly, the "Blind Spot Pass" seems poised to deliver tremendous results. Let me conclude with a quote from Thariq Shihipar:

"Fable is the first model where I find the quality of the work is bottlenecked by my ability to clarify its unknowns."

Here at Toshi Stats, we plan to take on various tasks using Fable5. Stay tuned!

You can enjoy our video news “ToshiStats AI Weekly Review” from this link, too!

1) A Field Guide to Fable: Finding Your Unknowns, 4,July 2026, Thariq Shihipar, Anthropic PBC

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