How can I adjust prompts or settings to get GPT-5's extended reasoning to match my task needs

Structuring Precision Instructions

The single most powerful principle for influencing GPT-5's reasoning is the clarity and precision of the prompt itself. GPT-5 strictly adheres to instructions; ambiguous or internally contradictory prompts will lead to inconsistent or superficial responses. When crafting prompts:

- State your objective directly. For example, “Analyze this legal argument from three philosophical perspectives, with citations,” is superior to “Analyze this argument.”
- Avoid conflicting directives. Don't combine “be concise” with directives that require exhaustive detail. This forces the model to arbitrate priorities or hedge output, dampening advanced reasoning.
- Explicitly sequence tasks. For extended reasoning, instruct the model to “break the problem into discrete steps, explain the rationale behind each, and synthesize a final assessment.”
- Ask the model to critique itself. Meta-prompts like “Review your answer for logical consistency and identify three weaknesses” push deeper reflection and self-correction.

Removing ambiguity is so central that OpenAI's recommended workflow suggests using their prompt optimizer: paste your prompt, and the tool will highlight ambiguous or conflicting phrasing and suggest direct edits. This is especially helpful when scaling prompts for organizational or multi-user scenarios.

Reasoning Effort: Tuning Depth and Pace

A remarkable advance in GPT-5 is the reasoning_effort parameter, which allows explicit control over how deeply the model engages with a task before producing an output. GPT-5 exposes four levels of reasoning effort:

- Minimal: The model performs the sparsest possible inference, suitable for deterministic tasks (e.g., extraction, simple rewrites). Prompts at this level should provide exhaustive structure and context, as the model will not pause to clarify missing details.
- Low: Slightly more thought, mainly for moderately structured outputs like customer support or summarization. It will take some initiative, but still prioritizes speed.
- Medium: The default. Provides a balance between speed and deliberation, suitable for most content creation and analysis. This is typically where “chain-of-thought” reasoning emerges naturally, with the model explaining its logic.
- High: Instructs GPT-5 to exhaust all plausible lines of reasoning and tool-calling before concluding. This is ideal for tasks where accuracy and nuance are critical—academic work, complex planning, debugging.

These effort levels can be selected in the Playground or API and reinforced within prompts: for high reasoning, encourage, “Take as many reasoning steps as needed to ensure thorough analysis before concluding.” For minimal, clarify, “Do not infer beyond the provided context.”

Controlling Agentic Eagerness

Extended reasoning can be further modulated by calibrating “agentic eagerness”—how proactive and persistent GPT-5 is in pursuing a task before yielding to the user:

- To increase model autonomy, combine high reasoning effort with prompts like: “Continue elaborating on your response until you are confident the problem is resolved. Do not stop or request further clarification unless you have exhausted all options.”
- To streamline responses, set lower reasoning_effort and specify, “Bias towards completing the task efficiently even if some uncertainties remain. Mark the result as provisional if context is not sufficient.”

The practical implication is a much more customizable agentic experience. An autonomous model is beneficial for research, troubleshooting, or multi-hop reasoning; a restrained one is best for routine data tasks and front-line support.

Verbosity: Output Length Versus Depth

GPT-5 also introduces a verbosity parameter, which is now independent from reasoning effort. This allows for precise control over output length—a common challenge in older GPT models:

- Low verbosity: Brief, direct answers; suitable for lookups, alerts, or anything where conciseness beats exposition.
- Medium verbosity: More context, supporting arguments, and explanations—ideal for technical overviews or decision-support.
- High verbosity: Detailed, exhaustive outputs; optimal for full reports, in-depth analysis, or knowledge transfer.

The beauty of this separation is that one can produce a high-reasoning, low-verbosity answer (concise but deeply reasoned), or the reverse, to match different consumption contexts without prompt hacks.

Recursive Self-Improvement and Meta-Prompting

Recent best practices, validated by advanced prompt engineers, recommend using recursive prompting or instructing the model to iteratively self-critique and improve its output. This is particularly effective for reasoning-intensive, multi-stage tasks:

1. Begin with an initial solution draft.
2. Instruct the model to evaluate its own output, identify weaknesses, and propose corrections.
3. Repeat the cycle for several rounds, each time focusing on a new aspect (e.g., logic, evidence, clarity).
4. Conclude with a final, synthesized response reflecting all improvements.

Explicitly define quality metrics for each review step—e.g., “In the first round, focus on factual accuracy; in the second, clarity; in the third, potential ethical concerns.” This multi-pass prompting forces deeper internal reasoning as the model cross-examines itself for varied criteria.

Structuring Complex Tasks: Decomposition and Context Management

For large, multi-part problems or tasks requiring interdisciplinary synthesis, guiding the model through structured decomposition is critical:

- Ask GPT-5 to “break down the task into sequential subcomponents.”
- For each, prompt it to “explain the rationale for addressing this part in this order.”
- After each part, summarize interim findings before proceeding to the next.
- Instruct to “synthesize an integrative conclusion at the end.”

This approach, akin to a “chain-of-thought” but structured at a higher meta level, greatly boosts reasoning quality and tractability, especially when the context window must support long or branching tasks.

Safeguards, Negative Examples, and Edge-Case Handling

A sometimes-overlooked dimension of advanced prompting is the inclusion of negative examples and explicit *edge-case instructions*:

- “If you encounter conflicting evidence, note and reconcile it in your response.”
- “Avoid generic responses; highlight any uncertainties and describe how they could be resolved.”
- “If any requirement is under-specified, proceed with the most likely assumption and document it at the end.”

These instructions prevent overgeneralization and force the model to acknowledge ambiguity, strengthening its reasoning chain.

Prompt Optimizer and Automated Quality Checks

Use the prompt optimizer (in Playground or equivalent platform): paste the prompt, review flagged ambiguities or impracticalities, and integrate suggestions directly. For long-form or production prompting:

- Regularly test prompt outputs against reference answers.
- Use meta-instructions: “After providing your answer, check for bias, logical error, and completeness, and flag any concerns.”
- Set up versioning for prompts as they evolve to accommodate new requirements or edge cases.

Examples of Prompts for Extended Reasoning

- High Reasoning, Autonomous Analysis:
“You are a research assistant. Your task is to answer the user's query entirely before yielding back. Use whatever reasoning, deduction, or tool-calling is needed. Do not terminate until all plausible routes are exhausted; summarize findings and annotate any assumptions made.”
- Efficient, Minimal Reasoning:
“Extract the main topic and sentiment from the following. Do not infer beyond explicit content. If uncertain, mark as ‘unknown.' Return only the requested fields, no extra commentary.”
- Recursive Self-Improvement:
“Draft a technical solution to the problem below. Critique your draft for completeness and error. Revise the response accordingly. Repeat up to three times, focusing on different error categories each iteration, then deliver your most accurate version.”

Common Pitfalls and Remedies

- Contradictory Prompts: Double-check before execution or use the optimizer for flagging.
- Lack of Stop Conditions: Especially for agentic use, define clear “end of turn” criteria or maximum steps.
- Output Drift: If GPT-5 starts to generalize or hedge, reinforce instructions around explicit accountability, e.g., “If in doubt, list all assumptions at the end.”
- Eliding Context: For complex tasks, always remind the model to refer to earlier outputs or conversation state. Example: “Continue using the framework established in step 1 for all subsequent steps.”

Advanced: Tool-Call Budgets and Uncertainty Policies

Especially for multi-hop queries or research tasks, set explicit limits:

- “You may call external tools a maximum of 10 times to gather supporting evidence. If context remains insufficient, produce a provisional recommendation, enumerate unknowns, and mark as provisional.”
- This both limits resource overhead and prevents endless circling for perfect certainty, which can be computationally expensive and unnecessary.

Empirical Testing and Iterative Optimization

- Test response quality at all four reasoning effort levels on a representative task cohort.
- Solicit model-led prompt evaluations: “Analyze this prompt and suggest improvements for better task fit.”
- Monitor outputs for stability, hallucination, and reproducibility. For complex reasoning, it's appropriate to run five or more output samples per prompt variant to catch non-determinism and identify optimal settings.

Final Recommendations

Deploying GPT-5 for extended reasoning that exactly matches the task requires deliberate tuning of prompt language, model parameters, and a willingness to empirically iterate. Always:

- Define explicit goals and constraints.
- Use the prompt optimizer and meta-prompting.
- Adjust reasoning effort and verbosity for your use case.
- Encourage the model to self-critique and decompose complex tasks.
- Regularly check for ambiguity, bias, and output consistency.