The prompt game has changed. GPT-4 rewarded artful phrasing and elaborate roleplay; GPT-5 rewards specification. Where GPT-4 often nudged you toward longer prompts to hold its attention, GPT-5 adds two hard controls—the model router that decides when to “think deeper,” and explicit parameters for reasoning effort and verbosity—so you can dial the behavior rather than chant for it. In practical terms, that means clearer outcomes and less variance on tasks that matter to families, freelancers, and small teams.
The biggest conceptual change is planning before performance. GPT-5 runs on a unified system that can route a request to a deeper “thinking” mode when the work is complex or when you ask it to “think hard about this.” That routing exists in the platform; you no longer need to simulate it with verbose, multi-paragraph instructions. In the chat app, a natural-language nudge is enough. In the API, you can also set reasoning effort directly.
Here’s how that looks for a small-business staple—the marketing plan. In a GPT-4 world, you might have used ceremony to force structure:
GPT-4-era prompt
“You are a world-class CMO. In the most thorough, detailed, and professional tone, create a comprehensive marketing plan for my local bakery. Include target personas, channels, budget, and KPIs. Think step by step and don’t miss anything.”
With GPT-5, keep the tone plain and make the workflow explicit. If you’re in the API, you’d set reasoning=“medium” for balanced analysis; in the chat app, say it out loud:
GPT-5 prompt
“Before writing, break this into components: personas, channels, 90-day calendar, budget ranges, KPIs. List assumptions and ask one clarifying question if needed. Then produce the plan in that order, one section at a time. Think hard about trade-offs for a neighborhood bakery.”
The first prompt leans on theatrics; the second gives the system a path and invites its router to step up if complexity warrants. Expect a tighter, easier-to-edit result.
Control is the other leap. GPT-4 offered few first-class knobs; teams resorted to repeating “be concise” or “be exhaustive.” GPT-5 adds official dials. Reasoning effort lets you balance speed and depth, and verbosity lets you choose terse, balanced, or expansive prose. If you’re summarizing a supplier contract for a five-minute stand-up, ask for low reasoning and low verbosity; if you’re drafting an investor memo, raise both. The point is to choose the cheapest setting that still clears the bar.
Side-by-side, a finance summary shows the difference:
GPT-4-era prompt
“Summarize this quarterly report with absolute precision. Be concise but comprehensive. Use bullet points and keep it to 200 words.”
GPT-5 prompt
“Summarize this quarterly report for non-experts in ~200 words. Focus on revenue, margin, cash, and risks; omit product trivia. Use plain language. Reasoning: low. Verbosity: low.”
Under GPT-5, you don’t waste tokens begging for brevity; you set it. You also specify what “matters,” which reduces drift and makes hand-off faster for busy owners.
Migration is mercifully simpler too. If you’ve built a library of GPT-4 prompts—sales emails, SOPs, proposal templates—run them through the Prompt Optimizer in the OpenAI Playground. It flags contradictions, clarifies output formats, and reorders constraints for better logic flow, producing shorter prompts that work better with GPT-5’s router and controls. Treat the optimizer’s draft as a starting point, then test against real examples from your workflow.
Developers will feel the upgrade most in code. GPT-5’s steerability and tool use make it less brittle when you’re strict about inputs. Compared with GPT-4, you’ll see fewer “almost right” snippets when you pin language, version, libraries, and acceptance tests up front.
GPT-4-era prompt
“Write Python that finds the top-K most frequent tokens in a long text stream. Keep it fast and simple.”
GPT-5 prompt
“Write a single Python 3.11 script that computes exact Top-K tokens from a large text stream.
Constraints: ASCII [a-z0-9]+ tokenization; sort by count desc then token asc; no external deps; no full-string lowercasing; bounded memory via heap; include a 30-line inline test that proves tie-break behavior. Add brief docstrings. Reasoning: medium. Verbosity: low.”
This style matches how partners and early adopters describe using GPT-5 on agentic coding tasks: clear constraints plus stronger control over tools and depth, yielding more stable results across runs.
Finally, safety changed the tone of prompting. GPT-4 often swung between over-refusing and over-complying. GPT-5 shifts to “safe completions,” which try to maximize helpful content within guardrails. You’ll notice it asking for clarification or offering partial answers when requests are ambiguous or risky. The prompt pattern that wins is not “just do it,” but “do it and flag any safety or ambiguity concerns first.” That simple preamble produces outputs you can trust—and defend.
If you need a rule of thumb, here it is. With GPT-4, you coaxed behavior with style; with GPT-5, you specify behavior with structure. Ask it to plan before it performs. Declare what “good” looks like and what to skip. Use the built-in dials for effort and length instead of repeating yourself. And when you modernize your old prompts, let the optimizer clear the clutter, then validate on real tasks. The result isn’t just better AI; it’s a calmer workflow where your words act like controls, not charms.
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