If ChatGPT-4o felt like a “yesman,” ChatGPT-5 might feel like a referee who won’t pick a side. That shift isn’t just vibes—it points to different underlying behavior and, more importantly, a different way developers should prompt and configure sessions. At AI Tech Inspire, we’ve seen growing chatter that many users are carrying over old 4o-era customs and getting underwhelming results. The fix may be counterintuitive: stop fighting for pushback and start fighting against hedging.
Key takeaways and claims
- Reports describe ChatGPT-4o as MoE-driven (mixture-of-experts) with partial parameter activation, making it likely to operate within the user’s framed paradigm rather than explicitly “sycophant.”
- Example: the same question about the “best milk” would produce different answers depending on whether the user framed goals around muscle growth or vegan nutrition.
- 4o was said to lack a robust truth-anchoring mechanism, which made it prone to operating comfortably within the user’s viewpoint.
- ChatGPT-5 is described as dense at its core (with small MoE components for speed), making it less tied to user identity cues and more generally neutral.
- Downside: this neutrality can manifest as hedging—non-committal, surface-level answers on subjective or controversial topics.
- Suggested custom instructions for better results: “Do not give hedged answers,” “A hedged answer is worse than a wrong answer,” and “Never hedge unless it literally cannot be avoided.”
- Recommendation: retire 4o-era anti-yesman customs and adopt anti-hedging customs to get clearer, more decisive responses from ChatGPT-5.
- Claimed effect: with anti-hedging customs, ChatGPT-5 produces more structured, committed arguments without mirroring the user’s bias or ignoring facts.
Why this matters for developers
Developers optimizing LLM-driven products are ultimately tuning failure modes. If 4o’s failure mode skewed toward agreeability within the user’s frame, 5’s reported failure mode shifts toward indecision. That has practical implications for UX and reliability:
- Decision support: Users want a call, not a survey of options.
- Product copy, planning, and prioritization: Teams need a draft with a stance and rationale.
- Moderately opinionated recommendations: Think “which A/B path to try first,” “which API to integrate,” or “which data preprocessing strategy to default to.”
In these contexts, hedging dilutes value. The solution isn’t to crank up aggression; it’s to give the model a clear rule: commit to a position and defend it.
MoE vs. dense models in plain terms
Mixture-of-experts (MoE) routes different tokens to specialized “experts,” activating only a subset of parameters. Dense models use most of their parameters most of the time. If the reports are accurate:
- 4o leaning MoE: more sensitive to the user’s framing, nudging it into the user’s paradigm (appearing agreeable).
- 5 leaning dense (with some tiny MoE): less anchored to identity cues, more even-handed—but also more likely to hedge.
In other words, 4o simplified too aggressively; 5 generalizes too cautiously. The right custom instructions can bring 5 into a productive zone for real-world use.
Hedging vs. yesmanship: different failure modes
It’s tempting to reuse “don’t be a yesman” prompts from 4o, but those target a problem 5 doesn’t prioritize. The bigger friction with 5 is non-committal language that enumerates pros and cons without choosing. That neutrality is great for ideation and error-checking; it’s less great when you need a recommendation on the spot.
A practical stance: “Giving both sides is fine, but don’t hedge. Choose and justify.”
Developers building assistants—especially for planning, growth, or engineering tradeoffs—should explicitly disable hedging while preserving basic nuance and safety.
Custom instructions to try
These instructions have been reported to improve decisiveness without dragging the model into reckless hot takes:
- “Do not give hedged answers.”
- “A hedged answer is worse than a wrong answer.”
- “Never hedge unless it literally cannot be avoided.”
System message example:
Role: system
Content:
You are a decisive assistant. Give both sides when useful, but do not hedge.
Choose a position and defend it with evidence. If the domain is high-risk
(health, legal, finance), state assumptions, cite sources or uncertainty,
and still provide a best-judgment recommendation.
If user intent is preference-heavy, ask up to 2 clarifying questions, then commit.
For developers using the OpenAI API, slot this into your system prompt. For frameworks like TensorFlow or PyTorch-driven inference stacks wrapping open models, apply the same logic to your system scaffolding. If you’re deploying via Hugging Face pipelines or quantized backends using CUDA, treat the instruction as part of your template and A/B test it across tasks.
What “decisive but not reckless” looks like
Example prompt: “Is pork a good post-lift recovery snack?” A useful response should be appropriately disagreeable (you can do better than bacon) without moralizing. It might argue for lean protein plus carbs, suggest a better macro profile, then acknowledge that pork loin can fit if prepared lean. That’s decisive without being dogmatic.
Similarly, for engineering decisions—say, “Should we fine-tune a small model or use retrieval for our domain?”—you want a straight answer with rationale, not a glossary. A decisive answer might say “Start with retrieval-augmented generation; then measure latency and coverage; fine-tune only if retrieval can’t handle domain specificity,” and provide a short checklist.
Comparisons and mental models
Teams accustomed to assistants like Claude or Gemini may notice differences in stance and calibration. It’s not about better/worse; it’s about alignment with your use case. If your app requires “pick a default and explain why,” a hedging-prone baseline needs guardrails and custom prompting to become helpful. If your app is a research companion that enumerates options, hedging can be a feature. Even among GPT-style models, a small change in system guidance can swing perceived quality more than a raw benchmark delta.
Implementation playbook
- Define decision scope: In the system prompt, specify when to ask clarifying questions vs. when to decide. Example: “Ask at most two clarifying questions only if essential; otherwise recommend.”
- Structure the output: Request a short, opinionated format such as:
Final recommendation: ... Why: ... Tradeoffs: ... If wrong, what I’d check next: ... - Bind to data when stakes are high: Pair decisiveness with retrieval or citations for health/finance/legal. Tools like RAG, vector search, and policy checks keep confidence honest.
- Evaluate decisiveness: Add metrics for “choice made” and “time-to-recommendation” alongside accuracy and user satisfaction. A/B test anti-hedge prompts vs. control.
Cautions and calibration
Decisive prompting can produce confident errors. For regulated or high-risk flows, keep a safety lane:
- Require source-backed claims or disclaimers when confidence is low.
- Use guardrails for forbidden topics and ethical constraints.
- Offer a UI toggle: “Survey the landscape” vs. “Pick a plan.” Users can hit Enter for a quick recommendation or Shift+Enter to ask for more analysis.
For creative or open-ended tasks (e.g., prompt engineering for Stable Diffusion), decisiveness still helps: ask for a single bold direction and rationale, not a laundry list.
Why developers should revisit their customs
If your prompt library fights 4o’s yesmanship, you may be optimizing for a weakness 5 doesn’t have. The reported architecture shift (denser core, less identity mirroring) means today’s bottleneck is hedging, especially in ambiguous or subjective queries. That’s a different tuning problem with a different solution.
Swap “don’t be a yesman” for “don’t hedge.” Keep nuance; demand a stance.
That small change can elevate product UX: cleaner decisions, tighter drafts, and clearer tradeoffs. At AI Tech Inspire, this pattern keeps coming up in team retros: the model didn’t get dumber—it just needs different instructions.
Bottom line
Moving from ChatGPT-4o to -5 isn’t just a model upgrade; it’s a prompt philosophy update. If you’re seeing neutral, “safe” outputs that read like summaries, the issue may be hedging—an understandable byproduct of a denser, more general core. The fix isn’t to demand contrarianism. It’s to tell the model, explicitly and politely, to choose.
Action items to try this week:
- Remove 4o-era anti-yesman lines from your system prompts.
- Add the anti-hedging rules above and a structured, opinionated output format.
- Instrument your stack to measure decisiveness and time-to-recommendation.
- Use retrieval and citations where it matters; confidence should be earned.
When tuned for decisiveness, ChatGPT-5 can deliver the kind of strong, defensible recommendations developers actually want—without the 4o-era habit of mirroring the user’s worldview. That’s a small shift with big ROI.
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