OpenAI has published a detailed set of prompting tips for GPT-6 Astra that includes a slop word blocklist, offering developers explicit guidance on how to shape the model’s behavior and writing style. The documentation, released on OpenAI’s developer portal, identifies specific tendencies in the model that can lead to unwanted outcomes — from excessive clarifying questions to overuse of certain phrases — and provides concrete prompts to correct them. For anyone building on OpenAI’s API, these recommendations represent a practical playbook for getting the most out of the company’s latest model.
GPT-6 Astra Asks More Questions, and That Creates a Trade-off
GPT-6 Astra marks a noticeable shift in how OpenAI’s models interact with users. Unlike GPT-5.6 Sol, which often made assumptions and pushed forward independently, GPT-6 Astra is designed to ask clarifying questions more frequently. OpenAI describes this as making the model a “more effective collaborator” — but the trade-off is that the model sometimes stops precisely where users expect it to keep going.
For developers accustomed to GPT-5.6 Sol’s more headstrong behavior, this can feel like a step backward. A model that pauses to ask for clarification every time it encounters ambiguity can break workflow momentum, especially in coding tasks or multi-step operations where the context should be sufficient to infer intent. OpenAI acknowledges this tension and provides a specific prompt designed to push GPT-6 Astra toward taking more initiative on its own.
What is the recommended prompt to make GPT-6 Astra take more initiative?
OpenAI recommends a prompt that tells the model to infer the user’s “intent” from the available context and to show a “bias towards action.” Phrases like “can you…,” “I want to…,” or “help me…” should be treated as direct calls to act, not as invitations for follow-up questions. The model is instructed to continue working persistently until the user’s intended goal is achieved, creating isolated work trees, resolving merge conflicts, performing read-only actions, and drafting pull requests independently — unless the actions are clearly destructive or irreversible.
The full recommended prompt reads: “You should infer the user’s intent and the scope of the task from the instructions and the conversation context so far. Your task is to demonstrate a tendency to act and to follow through on the user’s intended task until completion. If the user expresses the intention to complete new work or resolve an existing problem, continue working persistently until the user’s intended goal is achieved. Work independently toward the user’s goal (e.g., create isolated work trees/checkouts, resolve merge conflicts, perform read-only actions, create draft PRs, etc.), unless the actions are clearly destructive or irreversible.”
This prompt effectively rebalances the model’s default behavior from collaborative clarification toward proactive execution. Developers who want GPT-6 Astra to behave more like GPT-5.6 Sol in terms of taking initiative should adopt this framing early in their system messages.
Waiting for Approval Until a Result Is Ready
Another behavioral tweak concerns when and how the model asks for user approval. OpenAI’s guidance states that the model should wait to ask for approval until it has already prepared a concrete, reviewable result. The instruction is explicit: “The user should be approving a concrete, reviewable result.” This means the model should not ask for permission to start a task or to proceed through intermediate steps — it should do the work, produce an output, and then present it for review.
What should the model avoid including in its prompts?
OpenAI also advises removing unsolicited warnings, disclaimers, or safety checklists based on hypothetical risks from prompts. These additions tend to slow down interactions and can make the model appear overly cautious, undermining the user’s sense of control. The model should focus on delivering results rather than preemptively flagging risks that may never materialize.
GPT-6 Astra Is More Sensitive to Context — and to Contradictions in Skill Files
One of the more nuanced findings in OpenAI’s documentation is that GPT-6 Astra, while following longer instructions better than its predecessors, is also more sensitive to context. This sensitivity means that unclear or contradictory instructions in skill files — such as AGENTS.mdcodecodecodecode — can cause the model to block work or veer off unexpectedly. A single ambiguous sentence in a configuration file can ripple through a session, producing behavior that appears random but actually stems from a specific reading of the instruction.
OpenAI recommends auditing all skill files and context documents that the model can access, and giving user instructions explicit priority over any skill-based directives. This is especially important in multi-agent setups where different agents may draw on different skill files, increasing the risk of conflicting guidance.
For debugging these issues, OpenAI provides a targeted prompt: “If a skill causes you to ask for permission or confirmation, pause, leave requested work unfinished, or diverge from the user’s intent, name and link to the exact SKILL.md file you read, quote the relevant instruction, and briefly explain how it applies. Distinguish explicit skill requirements from your interpretation of guidelines.”
This debugging prompt forces the model to surface its reasoning, making it possible for developers to trace unexpected behavior back to a specific instruction in a specific file. It also requires the model to separate what the skill file actually says from its own interpretation of those guidelines — a distinction that can be critical when the model’s behavior seems to overreach or underreact.
The Slop Word Blocklist: “Delve,” “Foster,” “Leverage,” and More
OpenAI also addresses a topic that has become a running joke in the AI community: the predictable language patterns that language models fall into. GPT-6 Astra tends to structure responses with lists, tables, and Markdown formatting, and it reuses the same phrases across sessions. To counter this, OpenAI has compiled what it calls a “slop word” blocklist — a set of terms and phrases that the model should avoid using in its responses.
What are the slop words OpenAI recommends avoiding in GPT-6 Astra prompts?
The slop word blocklist includes phrases such as “Conclusion:” in conclusions, “delve into,” “promote,” “use/leverage,” “it’s worth noting,” “what’s important is,” “Question? Answer,” “This isn’t about X. It’s about Y,” “really/truly,” and compound descriptions and hyphenated adjectives. The model should also avoid summary closing statements like “In short:…” or “The simplest mental model is:…” and contrastive phrasing such as “X, not Y” or “X—not Y,” which introduces an unsolicited alternative the user did not ask for. Made-up compound terms like “exact-head checks” and “editorial-row layouts” should also be avoided, along with vague qualifiers and stock transitions.
The blocklist is designed to push the model toward simpler, more direct language. OpenAI recommends that the model state what it is doing rather than listing what it won’t do, and that it use simple verbs and prepositions to directly express the actual relationship. For technical writing specifically, OpenAI advises: “Use plain language over jargon, and reference technical details only to the degree that it helps illustrate an idea or your work to the user.”
Shaping the Model’s Writing Style Beyond the Blocklist
Beyond the blocklist, OpenAI provides more general guidance on writing style. If developers want prose rather than list-heavy output, the model should be told explicitly to write concise paragraphs using plain language and active voice. The recommended instruction is: “By default, use clear, concise paragraphs, each developing a single main idea. Use lists only if the information is truly parallel, sequential, or more easily comparable, and avoid nested lists unless the hierarchy cannot be clearly expressed in prose. Use simple, straightforward language: familiar words, concrete examples, and precise verbs. Favor the active voice and direct statements. Make the main point clear early on, then expand on it with the explanation and details the reader needs. Let each sentence build on the previous one. Develop the points that are important and provide enough evidence to be useful.”
This approach signals a broader shift in how OpenAI is thinking about model output quality. Rather than leaving style as an emergent property of the model’s training data, the company is now treating it as a first-class parameter that developers should configure explicitly. The slop word blocklist is a natural extension of that philosophy: if the model defaults to certain patterns, give developers the tools to suppress those patterns directly.
Sub-Agents Don’t Delegate Enough, and Tests Balloon Out of Proportion
GPT-6 Astra can hand off work to sub-agents running in parallel, but OpenAI has found that it does so less often than expected. This is a significant finding because parallel sub-agent execution is a key feature of the architecture, designed to speed up complex multi-step tasks. When the model fails to delegate effectively, it becomes a bottleneck, processing tasks sequentially that could be distributed across multiple agents.
Developers should spell out when and how much the model should delegate, OpenAI says. Leaving this to the model’s default behavior appears to result in under-delegation, which defeats the purpose of having a multi-agent system. Additionally, messages between agents can contain grammar or spacing errors, suggesting that the inter-agent communication channel is not as polished as the model’s direct output to users.
On coding tasks, GPT-6 Astra runs thorough tests before wrapping up. For small changes, that can mean test suites wildly out of proportion to the actual work — a single-line bug fix might trigger a full regression suite that takes minutes to complete. OpenAI recommends telling the model to rerun tests only when new failures or unresolved issues justify it. This small change can dramatically reduce latency for routine updates.
Practical Steps for Migrating to GPT-6 Astra
For developers who want to adopt GPT-6 Astra and apply OpenAI’s recommended prompts systematically, the company offers a practical shortcut. Using Codex with the OpenAI Docs skill, developers can run a single command: $openai-docs migrate this project to GPT-6 Astracodecodecodecode. This command applies the recommended changes automatically, including the prompting adjustments, the slop word blocklist, and the writing style configuration.
The OpenAI Docs skill is available in the curated skills directory on GitHub, and it provides a straightforward way to transition existing projects from earlier models. For teams managing multiple projects or complex agent configurations, this automated migration path can save significant manual effort.
More detailed versions of these prompts are available on the GPT-6 Astra model documentation page, which includes the full text of each recommendation along with examples and edge cases. Developers should treat this documentation as a living resource — OpenAI is likely to update it as more usage data accumulates and as the model itself evolves through fine-tuning and post-training adjustments.
The Strategic Significance of OpenAI’s Prompting Guidance
The release of this prompting guidance reflects a maturing understanding of how large language models behave in production environments. Early API usage often treated prompts as a one-time design choice — set the system message once and deploy. But as models become more capable and more sensitive to context, prompting is increasingly becoming an ongoing maintenance practice. Skill files, context documents, and user instructions all interact in ways that can shift model behavior unpredictably, and the debugging prompt OpenAI provides is a direct acknowledgment of that complexity.
The slop word blocklist is particularly revealing. It shows that OpenAI recognizes a problem that users have been complaining about for years: language models tend toward a narrow, predictable, and often grating verbal style. By encoding the blocklist as a prompt-level constraint, OpenAI gives developers a way to enforce stylistic quality without relying on post-hoc filtering or manual rewriting. This is a small but meaningful step toward making model output more natural and less AI-typical.
At the same time, the guidance on sub-agent delegation and testing behavior addresses practical inefficiencies that could limit adoption of GPT-6 Astra in enterprise environments. Under-delegation and over-testing are exactly the kinds of friction points that cause developers to revert to simpler, more predictable models. By surfacing these issues and providing concrete fixes, OpenAI is trying to smooth the path for wider deployment.
Looking ahead, the relationship between model behavior and explicit prompting will only become more critical as agents take on longer-running, more autonomous tasks. The ability to debug why a model paused, trace it to a specific skill file instruction, and adjust that instruction dynamically is the kind of operational capability that separates experimental use from production deployment. OpenAI’s latest documentation provides a framework for doing exactly that — and the slop word blocklist, for all its specificity, is really a symbol of a larger commitment to making model behavior both predictable and controllable.
