I've been reading OpenAI's comprehensive GPT-5 prompting guide [1], and it's fascinating how much the landscape of AI interaction has changed. The document is 40+ pages of detailed instructions for getting the best results from their latest AI system. This guide reveals that working with advanced AI is becoming more like managing a highly capable but occasionally unpredictable colleague than operating a simple tool.
The big takeaway for busy readers: AI systems like GPT-5 need careful management, clear instructions, and ongoing adjustment to work well. The days of just typing a question and getting a perfect answer are behind us. But the payoff for doing it right is significant.
Think about it this way. When you work with a new team member, you don't just give them tasks and walk away. You provide context, set expectations, give feedback, and adjust your approach based on their strengths. GPT-5 works similarly.
The guide positions GPT-5 as "a substantial leap forward in agentic task performance, coding, raw intelligence, and steerability" [1]. Yet beneath this confident language lies a more complex reality. These enhanced capabilities demand equally sophisticated handling techniques.
The Control Question: How Much Independence Should AI Have?
One of the most practical sections addresses what OpenAI calls "agentic eagerness" [1]. This is basically how much you want the AI to take initiative versus wait for your explicit instructions.
This matters more than you might think. Give AI too much freedom, and it might spend hours researching tangential details when you needed a quick answer. Give it too little, and it becomes a frustrating back-and-forth of constant clarification requests.
For fast results: The guide recommends setting clear time limits and scope boundaries. You can literally tell the AI: "Usually, this means an absolute maximum of 2 tool calls. If you think that you need more time to investigate, update the user with your latest findings" [1].
For thorough analysis: You can prompt for persistence: "Never stop or hand back to the user when you encounter uncertainty, research or deduce the most reasonable approach and continue" [1].
This flexibility exists because different tasks need different approaches. A quick competitive analysis requires speed over perfection. A strategic planning document demands thoroughness over speed.
The guide also introduces "tool preambles" - essentially asking the AI to explain its thinking as it works [1]. This serves two purposes: it keeps you informed about what's happening, and it forces the AI to think through its approach more carefully.
Real-World Lessons from Cursor's Experience
The most revealing section comes from Cursor, a company that builds AI-powered coding tools [2]. Their experience offers practical lessons for anyone working with AI.
The verbosity problem: Initially, GPT-5 provided detailed explanations for everything, which slowed down work. Cursor's solution was elegant: set low verbosity for general communication, but request high detail specifically for code creation [2].
Evolution of prompting strategies: Cursor discovered that prompting techniques that worked with earlier AI models became counterproductive with GPT-5. Their original instruction to "be THOROUGH when gathering information" led to excessive research and tool usage [2].
This reveals something important: as AI systems become more capable, our interaction strategies need to become more sophisticated, not more forceful. You guide rather than command.
The approval framework: Cursor developed an approach where the AI acts proactively but structures its work for easy review and modification. They tell the AI: "your code edits can be quite proactive, as the user can always reject" [2].
Three Modes of AI Thinking
The guide introduces three "reasoning effort" levels: minimal, medium, and high [1]. This concept should resonate with anyone who makes decisions about resource allocation.
Minimal reasoning: Appropriate for straightforward tasks where speed matters more than deep analysis. Like drafting routine communications or formatting documents.
Medium reasoning: Suits most everyday problems requiring some thought but not extensive analysis. Planning meeting agendas or summarising reports.
High reasoning: Reserved for complex, multi-faceted problems where thorough analysis is crucial. Strategic planning or comprehensive research projects.
This parallels how we naturally adjust our own cognitive effort based on task importance and complexity. You don't spend the same mental energy choosing lunch as deciding whether to change careers.
The Meta-Prompting Development
Perhaps the most intriguing recommendation involves using GPT-5 to optimise its own prompts [1]. The suggested approach asks the model to "explain what specific phrases could be added to, or deleted from, this prompt to more consistently elicit the desired behavior" [1].
This recursive application highlights an interesting paradox: as these systems become more powerful, they simultaneously become more complex to manage, potentially requiring their own involvement in that management process.
The irony is apparent but not necessarily problematic. Modern aircraft require computer assistance to fly effectively. Advanced medical diagnostics rely on AI systems to help doctors interpret results. If AI systems can genuinely improve human-AI interaction quality, this recursive application might be natural and beneficial.
Questions Moving Forward
The guide's technical depth raises questions about whether effective AI collaboration will become the province of specialists, potentially limiting benefits to those with resources to develop sophisticated interaction strategies.
The meta-prompting capability represents a development where AI systems become sophisticated enough to improve their own interaction patterns. While this offers practical benefits, it also raises questions about the boundaries of AI autonomy and human oversight, and you must have this awareness before the skillset can be enabled.
Perhaps most importantly, the guide demonstrates that AI interaction strategies must continuously evolve as capabilities advance. Static approaches to AI integration will become increasingly inadequate.
The Bigger Picture
The GPT-5 prompting guide presents a snapshot of a field in rapid transition - moving from simple tool usage towards complex partnership, from intuitive interaction towards systematic methodology.
Yet this complexity should be seen as capability rather than just barrier. The same sophistication that makes these systems challenging to manage also makes them capable of genuinely collaborative relationships with human users.
Success will depend less on achieving perfect technical implementations and more on developing effective ongoing relationships with AI systems. This relational approach - treating AI systems as sophisticated partners requiring clear communication and appropriate management - may prove more sustainable than attempting to control every aspect of AI behavior through rigid programming.
The questions raised about autonomy, complexity, and recursive improvement will likely become more pressing as AI capabilities advance. How well we navigate these questions may determine the effectiveness of our AI systems and the nature of human-AI collaboration for years to come.
Things to Test with ChatGPT-5 That Show How It's Different
Want to see GPT-5's capabilities for yourself? Here are practical experiments you can try that demonstrate the key differences from earlier AI models.
Test 1: The Autonomy Experiment
What to try: Give GPT-5 a complex, multi-step task with different autonomy instructions.
Version A (High autonomy): "Research the top 5 competitors in the electric vehicle market and create a comprehensive comparison. Don't ask me for clarification - make reasonable assumptions and keep working until you have a complete analysis."
Version B (Low autonomy): "Research electric vehicle competitors. Ask me for confirmation before proceeding to each new step. Maximum 2 research queries before checking in."
What you'll notice: GPT-5 will actually follow these autonomy instructions quite precisely, showing dramatically different behavior patterns between the two approaches.
Test 2: The Reasoning Effort Test
What to try: Ask the same complex question with different reasoning effort levels.
Question: "Our company wants to expand internationally. Analyze the pros and cons of entering the European vs Asian markets first, considering our $2M budget."
Test with: Minimal reasoning (for speed) vs High reasoning (for thoroughness)
What you'll notice: Minimal reasoning gives you a quick, surface-level analysis. High reasoning provides deeper strategic thinking, considers more variables, and offers more nuanced recommendations.
Test 3: The Meta-Prompting Challenge
What to try: Ask GPT-5 to improve its own instructions.
Start with: "Analyse this dataset and give me insights" (deliberately vague)
Then ask: "That prompt was too vague. What specific phrases should I add to get better, more actionable insights from you?"
What you'll notice: GPT-5 will actually suggest specific improvements to your prompting technique, essentially coaching you on how to work with it more effectively.
Test 4: The Persistence Test
What to try: Give it a research task that requires multiple sources and some problem-solving.
Example: "Find out what percentage of Fortune 500 companies have implemented AI ethics policies. If you can't find direct data, figure out alternative approaches to estimate this."
What you'll notice: Earlier models might give up or ask for help. GPT-5 will try multiple research angles, explain its reasoning, and work around data limitations.
Test 5: The Instruction Precision Test
What to try: Give it contradictory instructions and see how it handles them.
Example: "Write a brief summary that's also comprehensive and detailed. Make it both formal and conversational."
What you'll notice: GPT-5 will explicitly acknowledge the contradictions and either ask for clarification or explain how it's resolving the conflicts, rather than just picking one instruction randomly.
Try these tests yourself and you'll quickly understand why the 40-page prompting guide exists. This system has genuine depth that rewards thoughtful interaction.
Phil
Citations
[1] OpenAI. (2025). "GPT-5 prompting guide." Retrieved from internal OpenAI documentation. Specific quotes and technical recommendations regarding agentic eagerness, reasoning effort levels, tool preambles, instruction following precision, and meta-prompting techniques.
[2] Cursor Blog. (2025). "GPT-5's day-one integration into Cursor." Retrieved from https://cursor.com/blog/gpt-5. Details on verbosity parameter tuning, system prompt optimization, and production-level AI interaction strategies.