1 link tagged with all of: agentic workflows + prompt caching + chatgpt
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A practical guide to connecting Claude Opus 5.5 as an external tool to ChatGPT's Dots system, using it selectively for deep research, strict reviews, visual analysis, and long-running tasks while keeping Dots as the orchestrator. The article walks through 10 specific upgrades to make the combined system cheaper and more capable.
- Opus 5.5 connects as a private backend tool called only when tasks need deeper context, stricter review, visual analysis, or patience — not as a model replacement inside Dots itself.
- The four weakest points in Dots (shallow reading, uniform effort levels, weak self-review, poor visual work) map directly to where Opus adds value, and each has a specific upgrade to fix it.
- Caching the unchanging prompt prefix (standing brief, house rules, schemas) with a one-hour TTL cuts costs dramatically for teams running multiple tasks per day, and compacting the context window before it fills prevents mid-task cutoffs.
- Sorting work by whether anyone is waiting lets you move patient tasks (weekly digests, backlog triage, doc sweeps) to off-hours at lower cost, and moving house rules into the repo eliminates contradictions across four separate places.