
OpenAI shared GPT-6 Astra prompting tips to reduce excessive questions.
The model also gets a slop word blocklist.
Developers can apply changes via Codex.
What happened
OpenAI's model documentation for GPT-6 Astra includes prompting tips to manage its tendency to ask clarifying questions, and a blocklist of 'slop words' to avoid in outputs.
Why it matters
GPT-6 Astra asks clarifying questions more often than GPT-5.6 Sol, which can make it stop where users expect it to continue. OpenAI's guidance helps developers push the model toward action and avoid unwanted behavior.
What to watch
The test is whether GPT-6 Astra's "bias towards action" actually reduces its clarifying-question stops in real workflows, since the guidance depends on developers adopting the exact prompt wording. Watch whether OpenAI updates the model documentation page with further prompt revisions.
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OpenAI's documentation for GPT-6 Astra reveals a model that is more collaborative but also more cautious, sometimes stopping for clarification when users expect it to proceed. To address this, the company provides specific prompt language that tells the model to infer intent and act, while waiting to ask for approval until it has a concrete result. This reflects a design choice to make the model a better teammate rather than a pushover, but it requires developers to adjust their prompts.
The guidance also addresses the model's writing style, which tends toward lists and tables. OpenAI suggests instructing the model to use plain language and avoid 'slop words' — overused AI phrases like 'delve into' and 'leverage' — to produce more natural prose. This is part of a broader effort to make AI output less formulaic and more human.
For development teams, the documentation offers practical debugging tools, such as a prompt that forces the model to identify which skill file caused an unexpected pause. It also warns that GPT-6 Astra can over-test small changes, and advises telling it to rerun tests only when necessary. These tips aim to help developers integrate the model more smoothly into their workflows.
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