The Prompting Revolution: Your Guide to Mastering GPT-5

Every so often, a new technology arrives that isn’t just an incremental update but a fundamental shift in how we interact with machines. OpenAI’s new flagship model, GPT-5, feels like one of those moments. It represents a substantial leap forward in the capabilities that matter most for building real-world applications: agentic task performance, coding, and steerability.

But with great power comes a new level of complexity. The old tricks that worked for GPT-4 might now be counterproductive. In my own work building and deploying agentic systems, I’ve found that getting the best out of GPT-5 requires a new mindset. It’s less about simple instruction and more about a sophisticated collaboration with the model’s internal "thought" process.

Fortunately, OpenAI has released a comprehensive prompting guide, and I’ve spent the last week deconstructing its key insights. This isn't just another list of tips; it’s a peek under the hood of a next-generation AI. So, let's distil this technical manual into the strategic principles you need to know to truly harness the power of GPT-5.

Taming the Agent

The most significant change in GPT-5 is its enhanced capability as an "agent." It was trained with developers in mind, focusing on improved tool-calling, long-context understanding, and instruction following to power complex, multi-step tasks. But this new power comes with a new control dial: agentic eagerness. Think of this as a volume dial for the model’s autonomy. At one end, you can keep it on a tight leash with heavy programmatic control; at the other, you can delegate the vast majority of decision-making to the model. GPT-5 is designed to operate anywhere on this spectrum.

Prompting for Less Eagerness (More Control): By default, GPT-5 is thorough, sometimes to a fault. It will proactively gather context to ensure it produces a correct answer, which can lead to extra tool calls and increased latency. To rein it in for simpler, faster tasks, you can:

• Lower the reasoning_effort: A new API parameter that directly controls how hard the model thinks. For many workflows, medium or even low effort yields consistent results with better efficiency.

• Be Prescriptive in Your Prompt: Give the model clear criteria for how to explore a problem and when to stop. You can even set a fixed budget for tool calls (e.g., "an absolute maximum of 2 tool calls") to enforce a speed bias.

• Provide an "Escape Hatch": When you limit the model's ability to gather context, it's helpful to add a clause that allows it to proceed under uncertainty, like "provide the best possible answer even if it might not be fully correct"

Prompting for More Eagerness (More Autonomy): For complex, long-horizon tasks where you want the AI to be persistent, you can increase the level of autonomy. Increase reasoning_effort and use a prompt that encourages tenacity: <persistence>You are an agent, please keep going until the user's query is completely resolved. Never stop or hand back to the user when you encounter uncertainty.</persistence>.

This new level of control is a game-changer for building sophisticated agents. You can now fine-tune the model’s proactivity to match the specific risk and complexity of any given task.

Lessons from Cursor

GPT-5 is a coding powerhouse, capable of working in large codebases, fixing bugs, and implementing multi-file features from scratch. The official guide provides specific recommendations for frontend stacks (favouring Next.js, TypeScript, and Tailwind CSS). However, the most valuable insights come from a case study with the AI code editor Cursor, one of GPT-5’s trusted alpha testers. Their experience tuning prompts for a real-world product is a masterclass in practical prompt engineering.

Cursor’s team found that prompts which worked well for older models were now counterproductive. For instance, a prompt instructing the model to be "THOROUGH when gathering information" caused GPT-5 to overuse its search tools on simple tasks, as the model is already naturally proactive. They had to soften the language to get the model to make better decisions about when to use its internal knowledge versus reaching for external tools.

They also discovered a powerful hybrid approach to controlling output. They set the global verbosity API parameter to low to keep status updates concise, but then used a specific prompt instruction - "Write code for clarity first. Prefer readable, maintainable solutions..." -to encourage verbose, well-commented code only within the tool calls. This dual usage of a global parameter and a specific prompt resulted in a perfectly balanced user experience: brief text updates with highly readable code diffs.

Better Listening Requires Better Instructions

One of the most fascinating and counterintuitive aspects of GPT-5 is a direct result of its improved steerability. The model follows instructions with "surgical precision". While this is a huge advantage, it also means that poorly-constructed prompts containing vague or contradictory instructions are more damaging to GPT-5 than to other models.

An older, less precise model might randomly ignore one of two conflicting instructions. GPT-5, by contrast, will expend precious reasoning tokens trying to find a way to reconcile the contradiction, leading to inefficient or impaired performance. OpenAI's guide gives a perfect example from a healthcare context:

• A prompt states, "Never schedule an appointment without explicit patient consent".

• But it later says, "auto-assign the earliest same-day slot without contacting the patient".

This conflict sends GPT-5 into a loop. Fixing the contradiction by clarifying the hierarchy of rules (e.g., adding "Do not do lookup in the emergency case...") drastically streamlines its performance.

The key takeaway is that as models become more intelligent and better at following instructions, the burden of clarity and logical consistency shifts squarely onto the prompter. In many ways, this is a return to the fundamentals of conventional programming. The crucial skill is not just learning a new 'language' or syntax; it’s the timeless discipline of algorithmic thinking—the ability to break down a problem and express it in a logical, unambiguous sequence. (A skill that, arguably, a whole generation of computer science students missed out on, but that’s a topic for another day 😉 ).

Reviewing and refining your core prompts is no longer just good practice; it’s a critical step to unlocking the model's peak performance. And in a meta-twist, the guide recommends using GPT-5 itself as a "meta-prompter" to debug and optimise your prompts for it.

New API Parameters for Granular Control

OpenAI has externalised the model's internal control flow with two powerful new parameters:

• reasoning_effort: This is the most important new dial. You can set it from minimal (for fast, simple tasks) to high (to fully engage the gpt-5-thinking model for deep deliberation). It allows you to precisely manage the trade-off between speed, cost, and accuracy for any given task.

• verbosity: This parameter controls the length of the final answer, independent of the reasoning effort. This is a crucial innovation. You can now ask the model to perform a deep, high-effort analysis (reasoning_effort='high') but return only a single, concise answer (verbosity='low').

The new best practice is to use structured, XML-like tags to define the model's behaviour. This turns the prompt into a modular, machine-readable brief. Key tags include:

• <context_gathering>: Defines the AI's research strategy before it acts.

• <persistence>: Governs its level of autonomy and dictates its actions when it encounters uncertainty.

• <self_reflection>: A powerful technique where you instruct the model first to create its own rubric for a "world-class" solution, and then iterate on its work until it meets that high standard.

A New Era of Cognitive Collaboration

Mastering GPT-5 requires a shift in our approach. We are moving away from being simple taskmasters and toward becoming sophisticated architects of the AI's cognitive process. Our role is to provide clear, logically consistent, and well-structured specifications that this powerful new engine needs to operate at its peak. This role demands the timeless discipline of algorithmic thinking.

The era of casually "chatting" with a super-intelligent AI in a professional context is giving way to a more disciplined and powerful partnership. We're not just talking to a machine anymore; we're programming a mind with our words.

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Until next time, choose your words carefully—your new compiler is listening.