别再用旧方法驾驭新模型：Fable 5 与 GPT-5.6 Sol 的高杠杆工作法

Each major model jump requires users to retest old prompting habits because yesterday's optimizations can underuse or even degrade today's model.

A more tenacious agent needs explicit boundaries on sources, budgets, dates, approvals, and irreversible actions before it starts working.

Steering a model while it is working reduces collaboration latency and turns generation into an ongoing joint process rather than a sequence of isolated turns.

Unstructured voice dictation can supply more useful context than a polished typed prompt because it preserves assumptions, examples, and intent that concise notes omit.

Repeated legacy instructions can make GPT-5.6 Sol worse and more expensive, so each requirement should normally be stated once and tested for necessity.

The strongest model at maximum reasoning effort is not automatically the best choice; users should match model size and thinking depth to task difficulty.

Concrete writing behaviors produce more reliable tone than adjectives such as friendly or empathetic because they specify what the response should actually do.

The largest productivity gain appears when AI moves beyond clearing a dopamine backlog and begins supporting judgment-heavy impact work.

As agents become more capable, output quality is increasingly bottlenecked by the user's ability to reveal known unknowns, hidden assumptions, and unknown unknowns.

A useful agent loop needs a hard bar for completion, repeated self-checks, and a clear stop condition instead of letting the model decide that its first acceptable answer is done.