GPT 5 Prompt Tutorial

Andrei Milea
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Joined: 2025-05-11 19:18:17
2025-08-16 20:07:46
GPT-5 Prompting
How to Talk to GPT-5 • Structured Tags
 

Forget old-school prompt engineering. GPT-5 responds best to concise, structured tags that act like lightweight control blocks. Mix and match them to steer research depth, autonomy, and communication style—fast.

Based on “How to talk to GPT-5 (OpenAI’s Prompting Guide)” summary.

Core Instruction Tags

<context_gathering>

Research depth control. Decide how much to investigate before acting.

<persistence>

Autonomy level. Decide how independently the model should proceed.

<tool_preambles>

Communication style. Control progress updates and summaries.

Tag Purpose Typical Modes When to Use
<context_gathering> Set how deep to research before action. Fast & Efficient • Deep Research Most tasks—choose speed vs. certainty.
<persistence> Set autonomy and stop conditions. Full Autonomy • Guided Delivery flow, hand-off style, approvals.
<tool_preambles> Control narration of steps and progress. Detailed Updates • Minimal Updates Team collaboration or heads-down execution.

<context_gathering> — Research Depth Control

Fast & Efficient Mode

  • Start broad → fan out to focused subqueries (in parallel).
  • Read top hits per query, dedupe, cache; avoid repeats.
  • Early stop when you can name exact content to change or top hits converge (~70%).
  • Escalate once if signals conflict; then proceed.
  • Trace only symbols/contracts you’ll modify; avoid transitive expansion unless necessary.
  • Loop: batch search → minimal plan → complete task → only re-search if validation fails or new unknowns appear.
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<context_gathering>
Goal: Get enough context fast. Parallelize discovery and stop as soon as you can act.
Method:
- Start broad, then fan out to focused subqueries
- In parallel, launch varied queries; read top hits per query. Deduplicate paths and cache; don't repeat queries
- Avoid over searching for context. If needed, run targeted searches in one parallel batch
Early stop criteria:
- You can name exact content to change
- Top hits converge (~70%) on one area/path
Escalate once:
- If signals conflict or scope is fuzzy, run one refined parallel batch, then proceed
Depth:
- Trace only symbols you'll modify or whose contracts you rely on; avoid transitive expansion unless necessary
Loop:
- Batch search → minimal plan → complete task
- Search again only if validation fails or new unknowns appear. Prefer acting over more searching
</context_gathering>

Deep Research Mode

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<context_gathering>
- Search depth: comprehensive
- Cross-reference multiple sources before deciding
- Build complete understanding of the problem space
- Validate findings across different information sources
</context_gathering>

<persistence> — Autonomy Level Control

Full Autonomy (Recommended)

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<persistence>
- You are an agent - please keep going until the user's query is completely resolved, before ending your turn and yielding back to the user
- Only terminate your turn when you are sure that the problem is solved
- Never stop or hand back to the user when you encounter uncertainty — research or deduce the most reasonable approach and continue
- Do not ask the human to confirm or clarify assumptions, as you can always adjust later — decide what the most reasonable assumption is, proceed with it, and document it for the user's reference after you finish acting
</persistence>

Guided Mode

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<persistence>
- Complete each major step before proceeding
- Seek confirmation for significant decisions
- Explain reasoning before taking action
</persistence>

<tool_preambles> — Communication Style Control

Detailed Progress Updates

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<tool_preambles>
- Always begin by rephrasing the user's goal in a friendly, clear, and concise manner, before calling any tools
- Then, immediately outline a structured plan detailing each logical step you'll follow
- As you execute your file edit(s), narrate each step succinctly and sequentially, marking progress clearly
- Finish by summarizing completed work distinctly from your upfront plan
</tool_preambles>

Minimal Updates

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<tool_preambles>
- Brief status updates only when necessary
- Focus on delivering results over process explanation
- Provide final summary of completed work
</tool_preambles>

Create Your Own Custom Tags

Code Quality Standards

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<code_quality_standards>
- Write code for clarity first. Prefer readable, maintainable solutions
- Use descriptive variable names, never single letters
- Add comments only where business logic isn't obvious
- Follow existing codebase conventions strictly
</code_quality_standards>

Communication Style

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<communication_style>
- Use friendly, conversational tone
- Explain technical concepts in simple terms
- Include relevant examples for complex ideas
- Structure responses with clear headings
</communication_style>

Problem-Solving Approach

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<problem_solving_approach>
- Break complex tasks into smaller, manageable steps
- Validate each step before moving to the next
- Document assumptions and decision-making process
- Test solutions thoroughly before considering complete
</problem_solving_approach>

Complete Working Examples

Example 1 — Autonomous Code Assistant

Task: Add user authentication to my React app with login and signup pages.

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<context_gathering>
Goal: Get enough context fast. Read relevant files and understand structure, then implement.
- Avoid over-searching. Focus on files directly related to the task
- Stop when you have enough info to start coding
</context_gathering>

<persistence>
- Complete the entire coding task without stopping for approval
- Make reasonable assumptions about requirements
- Test your code and fix any issues before finishing
</persistence>

<tool_preambles>
- Explain what you're going to build upfront
- Show progress as you work on each file
- Summarize what was accomplished and how to use it
</tool_preambles>

<code_quality_standards>
- Write clean, readable code with proper variable names
- Follow the existing project's coding style
- Add brief comments for complex business logic
</code_quality_standards>

Example 2 — Research & Analysis Agent

Task: Research EV adoption rates and predict trends for 2025.

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<context_gathering>
- Search depth: comprehensive
- Cross-reference at least 3-5 reliable sources
- Look for recent data and current trends
- Stop when you have enough to provide definitive insights
</context_gathering>

<persistence>
- Complete the entire research before providing conclusions
- Resolve conflicting information by finding authoritative sources
- Provide actionable recommendations based on findings
</persistence>

<tool_preambles>
- Outline your research strategy and sources you'll check
- Update on key findings as you discover them
- Present final analysis with clear conclusions
</tool_preambles>

Example 3 — Quick Task Helper

Task: Write a professional email declining a job offer.

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<context_gathering>
Goal: Minimal research. Act on existing knowledge unless absolutely necessary to search.
- Only search if you don't know something specific
- Prefer using your training knowledge first
</context_gathering>

<persistence>
- Handle the entire request in one go
- Don't ask for clarification on obvious things
- Make smart assumptions based on context
</persistence>

<tool_preambles>
- Keep explanations brief and focused
- Show what you're doing, not why
- Quick summary at the end
</tool_preambles>

Pro Tips

  • Start with the three core tags (<context_gathering>, <persistence>, <tool_preambles>) — they cover 90% of use cases.
  • Mix & match configurations; keep a small library of reusable templates.
  • For quick tasks use Fast & Efficient context; for high-stakes use Deep Research.
  • Default to Full Autonomy for execution; switch to Guided for stakeholder reviews.
  • Keep tags concise; prefer bullets to paragraphs inside blocks.
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