I feel like @parmy & @frimelle are introducing a very important nuance in the sycophancy debate with this article. I'm sure this is very challenging, especially at chatgpt scale but in my opinion, there's a way to keep conversational AI warm/empathetic/somehow sycophantic while
GENERATIVE AI
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GPT-5 Thinking differs between Plus and Pro tiers
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GPT-5 thinking in plus tier is not the same as GTP-5 thinking in pro tier. That „thinking“ (reasoning) setting is higher. https://
x.com/scaling01/stat
/scaling01/status/1955610515134460285
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Prompts as the key to unlocking AI chatbot potential
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this is spot on, prompts are the key to unlocking chatbot potential
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Prompt Engineering as a Form of User Experience Design
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Prompt Writing is UX You’re not talking to a robot.
You’re designing how the AI thinks. Prompting is a language. Master it, and you control the conversation. -
Techniques to Reduce AI Model Hallucinations
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Bonus: Reduce Hallucination Hallucinations happen when models make stuff up. Fix it with: Retrieval Augmented Generation (RAG) – ReAct (reason + act)
– Chain-of-Verification Don’t just ask questions. Ask it to check its own answers. -
Using Logic-of-Thought to Enhance LLM Reasoning
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10. Expert: Logic-of-Thought (LoT) Inject formal logic. Prompt the model to identify, verify, and reason using rules like: If A implies B, and A is true, then B must be true. Perfect for law, ethics, science, structured thinking.
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Improving AI Model Reasoning with Chain-of-Code Prompting
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9. Expert: Chain-of-Code (CoC) Want precision? Ask the model to reason in pseudocode or actual code. Why? Code forces structure and logic.
It reduces fluff, boosts accuracy. Example: "Write code to solve this step by step…" -
Using Self-Refine Prompting to Improve LLM Output Quality
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8. Advanced: Self-Refine Prompt → Output → Self-Critique → Improved Output Let the model fix itself. Prompt: "Write a tweet. Now critique it. Now rewrite it based on your feedback." This loop improves clarity, tone, and logic.
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Using Auto-CoT for Scalable Reasoning
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4. Intermediate: Auto-CoT Don't want to write examples yourself? Auto-CoT does it for you. Prompt the model to generate its own demos: "Here are a few examples. Let’s think step by step." Now you’ve got scalable reasoning with less effort.
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Improving Model Reasoning with Chain-of-Thought Prompting
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3. Intermediate: Chain-of-Thought (CoT) Make the model "think step-by-step." This boosts reasoning dramatically. Instead of: "What's 13 * 17?" Try: "Let’s solve this step by step." It will explain its thinking before answering.