How to Get the Most Out of Fable 5 and GPT-5.6 Sol

Summary

A couple weeks into this new class of models, the tips and tricks are starting to pile up — and the common threads cutting across both Fable 5 and GPT-5.6 Sol point to more than just new prompting habits. They suggest new patterns of interaction with AI models altogether.

OpenAI's Official Prompting Guide for GPT-5.6 Sol: Codex team member Eric Provencher's guide centers on the idea that a more tenacious model needs explicit boundaries — telling it to use only supplied sources, stay within approved figures, or draft a message rather than send it. The guide also pushes iteration through follow-up messages rather than one-shot prompting, including new "steer" and "queue" controls as Codex behaviors come to the main ChatGPT app, plus separate best practices for chat versus work modes and a reminder that voice dictation is still one of the best ways to feed a model context.

GPT-5.6 Best Practices, Summarized: AI content creator Ollie Lehmann rounded up the sharpest tips from OpenAI's developer site: state each instruction once instead of stacking old rule lists (removing repeats reportedly raised scores 10-15% while cutting tokens up to 66%), match model size and effort level to the job rather than defaulting to max, and swap blanket brevity instructions for concrete guidance on tone and structure.

Christine Zhu: "You're Not Ambitious Enough with Claude": Intuit AI UX PM Christine Zhu argues most people use Claude to clear their "dopamine backlog" of small tasks and leave the real impact work to themselves. She maps three levels of work — optics, execution, and impact — to three different ways of using Fable 5: as autopilot for visibility work, copilot for weekly planning and status checks, and sparring partner for the highest-stakes strategic thinking.

Tariq: "A Field Guide to Fable: Finding Your Unknowns": A member of the Claude Code team lays out a framework for prompting Fable 5: known knowns (what's in your prompt), known unknowns (what you're aware you haven't figured out), unknown knowns (what's so obvious you'd never write it down), and unknown unknowns. His fix is discovering unknowns iteratively — with techniques like a "blind spot pass" to surface gaps in your own knowledge, and asking for several wildly different prototype directions when you don't know what you want until you see it.

Daniel Meissler's Tactical Meta Prompts: Daniel Meissler keeps a set of prompts to rerun with every new frontier model. One is a "self model audit" that has the model flag every place your own files or context are modeling a stale or aspirational version of you.