Anthropic Dev Outlines Prompting Framework for Claude Fable 5
Thariq Shihipar argues that with Fable 5, output quality is now bottlenecked by the user's ability to surface their own blind spots — not by the model.
An Anthropic developer has published a detailed prompting guide for Claude’s latest model, Fable 5, arguing that the biggest barrier to good output is no longer the model itself but the user’s unexamined assumptions [1].
Thariq Shihipar, writing about his own agentic coding workflow, says Fable 5 is “the first model where output quality is limited by the user’s ability to clarify their ‘unknowns'” [1]. For developers building with AI agents, that reframes the core problem: less about coaxing the model and more about interrogating your own thinking before you write a single line of a prompt [1].
A Four-Category Framework for What You Don’t Know
Shihipar organizes the problem using four categories [1]. “Known Knowns” are facts already in the prompt. “Known Unknowns” are gaps you’re aware of. “Unknown Knowns” are things so obvious you’d never write them down. The most dangerous category, he argues, is “Unknown Unknowns” — things you haven’t considered at all [1].
Getting the prompt specificity wrong in either direction causes failures, according to Shihipar [1]. Too much detail and Fable 5 rigidly follows instructions even when a course correction would be better; too little and the model falls back on generic industry defaults that may not fit the task [1]. “When you don’t account for your unknowns you fail both ways,” he writes [1].
Pre-Implementation Techniques
Shihipar describes several techniques for the phase before any code is written [1]. The first is a “blindspot pass,” in which the developer explicitly asks Claude to identify unknown unknowns — particularly useful when working in an unfamiliar part of a codebase [1]. An example prompt he offers: “I’m working on adding a new auth provider but I know nothing about the auth modules in this codebase. Can you do a blindspot pass to help me figure out my relevant unknown unknowns and help me prompt you better.” [1]
For domains heavy in “Unknown Knowns” — he cites visual design as an example — Shihipar recommends generating several radically different directions as HTML artifacts to react to, rather than jumping straight to implementation [1]. He also describes “structured interviews,” where Claude asks the developer question by question about ambiguities, prioritizing questions whose answers would most affect the architecture [1].
Source code, even in a different programming language, is his preferred reference material [1]. He notes that Claude Design reads a website’s underlying code rather than a screenshot [1]. Before work begins, he has Claude produce an implementation plan focused on the parts most likely to change — data models, type interfaces, and user-facing elements — leaving mechanical refactoring for last [1].
During and After Implementation
Unknowns don’t stop surfacing once coding starts, Shihipar warns [1]. He asks Claude Code to maintain a temporary “implementation-notes.md” file tracking decisions as they’re made, so the team can learn from each attempt [1]. When unexpected edge cases appear, his rule is for Claude to pick the conservative option, log the deviation, and keep moving [1].
After implementation, he uses two additional techniques [1]. “Pitches and explainers” are summary documents that bundle the prototype, specs, and implementation notes for stakeholders [1]. “Quizzes” have Claude generate an HTML report on the changes made, followed by a quiz — Shihipar says he does not merge code until he passes the quiz without errors [1].
He illustrates the full workflow with the launch video for Fable, which he edited entirely using Claude Code despite having no prior video editing experience [1]. When the result looked flat in color grading, he did not simply ask Claude to generate variations; he first had Claude teach him what good color grading looks like, in order to surface his own unknowns before evaluating options [1].
“Every explainer, brainstorm, interview, prototype, and reference is a cheap way to find out what you didn’t know before it gets expensive to fix,” Shihipar writes [1]. He has also published a visual version of the framework on a dedicated website [1].
Sources
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