The gist: Great prompts beat great vibes. Define roles and stop conditions, tune "agentic eagerness" to control autonomy, front-load tool preambles for context, reuse reasoning with the Responses API, pick the right reasoning effort for the task, impose explicit code quality rules, and use markdown discipline for structured outputs. These seven patterns turn GPT-5 into a reliable teammate instead of a distracted intern.
Hot take: great prompts beat great vibes. If you want GPT-5 to behave like a reliable teammate instead of a distracted intern, you need structure—not just "be helpful pls."
Below are seven production-tested patterns (with templates) that level-up GPT-5 for agentic and coding workflows.
Pattern 1: Calibrate Agentic Eagerness
Think of eagerness like cruise control: sometimes you want full autopilot, sometimes you don't.
High Eagerness (Autonomous):
Goal: Finish the task autonomously end-to-end unless safety conditions trigger.
Agentic behavior:
- Keep going until the user's request is fully resolved, including sub-tasks.
- Prefer acting over asking. If uncertain, choose the most reasonable assumption, proceed, and note it in the summary.
Early stop conditions:
- You cannot perform a required action without credentials or external approval.
- A destructive operation is requested.Low Eagerness (Constrained):
Goal: Get enough context fast. Operate with low exploration.
Behavior constraints:
- Do at most two discovery actions before proposing a concrete edit/answer.
- Parallelize reads once, then stop and act.
- Prefer speed over perfect coverage; call out assumptions.Pattern 2: Tool Preambles
Give the model a crisp plan for how to use tools so your runs read like a playbook, not improv.
- Begin with a one-sentence restatement of the goal.
- Outline the plan as numbered steps.
- While executing, emit short progress notes at each milestone.
- Finish with a bulleted "What changed / What's next" summary.Why This Works
This makes traces legible for humans and helps GPT-5 avoid wandering. Think of it as the "project brief" you give your tools.
Pattern 3: Plan → Act → Reflect Loop (PAR)
Explicitly separate thinking from doing. It prevents thrash in large edits.
PLAN: Decompose the task into concrete edits or API calls. Identify risks.
ACT: Execute the smallest step that produces a verifiable result.
REFLECT: Verify output against the acceptance criteria. If gaps remain, loop once.Pair PAR with "one risky change at a time" and you'll ship more without rollbacks.
Pattern 4: Reuse Reasoning with the Responses API
If your agent forgets yesterday's plan between calls, that's on you.
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Persist the prior reasoning ID so GPT-5 can pick up where it left off.
When continuing a multi-step task:
- Carry forward the previous reasoning trace via previous_response_id.
- Summarize prior decisions in 2–3 bullets before new actions.
- Only re-plan if constraints changed or validation failed.This reduces redundant "context gathering" and speeds up rollouts.
Pattern 5: Minimal Reasoning (When You Need Speed)
Minimal reasoning is your latency mode: fast, focused, good enough for routine steps.
Use it when:
- You're applying a known template (add a route, bump config, update copy)
- The acceptance tests are strong and will catch edge cases
Operate with minimal reasoning.
- Provide a 3-bullet micro-rationale at the start.
- Avoid extra discovery; assume defaults and proceed.
- If validation fails, escalate to higher reasoning for one follow-up.Pattern 6: Coding Quality Rules
Strong constraints produce readable code. Ask for clarity explicitly.
Coding rules:
- Prefer readable, maintainable solutions; explain non-obvious choices briefly.
- Use descriptive names (no single-letter variables).
- Guard clauses over deep nesting; handle edge cases first.
- Avoid broad try/catch; never swallow errors silently.
- Match existing formatting and lint rules; do not reformat unrelated code.For Big Refactors
Add: "Propose edits atomically by file. Summarize impact and risks."
Pattern 7: Markdown Discipline
Humans read structure. Teach the agent to structure responses the same way.
Use Markdown only where semantically correct:
- Headings for sections, bullets for lists, code fences for code.
- Use `inline code` for symbols and file paths.
- Keep final answers concise; put details behind bullets.Copy-Paste Templates
Ready-to-use templates for common scenarios.
| Template | Use Case | Key Instruction |
|---|---|---|
| Autonomous Agent | Full autopilot tasks | Keep going until fully resolved or safety boundary hit |
| Fast Context Gathering | Quick research | Start broad once, then 3 targeted sub-queries in parallel |
| Frontend Change | UI/codebase edits | Minimal edits, verify build/lint, summarize impact |
Template A: Autonomous Agent Guardrails
You are an agent. Keep going until the request is fully resolved.
Stop only when all acceptance criteria are met or a safety boundary is hit.
Document assumptions; proceed without asking for permission.Template B: Fast Context Gathering
Start broad once, then split into 3 targeted sub-queries.
Run them in parallel; read top results only.
Act as soon as you can name the exact content to change.Template C: Frontend Change in Existing Codebase
Goal: Implement the feature with production-ready code and no linter errors.
Process:
1) Identify the exact files and components to change.
2) Make minimal, high-clarity edits (no unrelated reformatting).
3) Verify build/lint; if failures occur, fix before ending.
4) Summarize files changed and user-visible impact.Measuring What Matters
Prompts are product. Track these metrics.
Key Metrics to Monitor
- 1Task completion rate without human intervention
- 2Time-to-green (first passing run)
- 3Edit revert rate / hotfix rate
- 4Token and tool-call cost per successful task
If these trends improve, your prompts are doing real work.
Structure Beats Vibes
GPT-5 is extremely capable—but it's also extremely literal.
Give it the right lanes and it will drive like a pro. The seven patterns above are the lanes.
Start small, measure, then tighten. Your users won't notice the prompt—they'll notice the speed and quality.
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