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5 GPT-5 Prompting Tips to Fix Worse Results After ChatGPT-5 Update

Content Introduction

This guide explains why GPT-5 performs worse with old prompts and provides five proven techniques to dramatically improve outputs, covering model architecture changes and advanced prompting strategies.

Key Information

  • 1GPT-5 uses model consolidation with an invisible router that doesn't always select the best model
  • 2GPT-5 follows instructions more precisely but is worse at guessing intent from vague prompts
  • 3Router nudge phrases like 'think hard about this' force selection of higher reasoning models
  • 4Verbosity control phrases manage output length for different use cases
  • 5XML tags structure prompts for better comprehension by GPT-5
  • 6Perfection loop makes GPT-5 self-critique and iterate internally before delivering final output

Content Keywords

#Router Nudge Phrases

Specific phrases that force GPT-5's invisible router to select higher reasoning models

#Verbosity Control

Techniques to control output length for different communication needs

#OpenAI Prompt Optimizer

Official tool that rewrites prompts for better GPT-5 performance

#XML Tags

Structured formatting that helps GPT-5 better understand prompt components

#Perfection Loop

Method that makes GPT-5 self-critique and iterate internally before final output

#Model Consolidation

GPT-5's architecture change that reduced multiple models to three main options

Related Questions and Answers

Q1.Why are old prompts performing worse in GPT-5?

A: GPT-5 has model consolidation with an invisible router that doesn't always select the best model, and it follows instructions more precisely but is worse at guessing intent from vague prompts.

Q2.How can I force GPT-5 to use better reasoning models?

A: Use router nudge phrases like 'think hard about this', 'think deeply about this', or 'think carefully' at the end of your prompts to trigger deeper reasoning.

Q3.What's the best way to control GPT-5's output length?

A: Use specific verbosity control phrases: 'give me the bottom line in 100 words or less' for low verbosity, 'aim for concise 3-5 paragraph explanation' for medium, and 'provide comprehensive 600-800 word breakdown' for high verbosity.

Q4.How do XML tags improve GPT-5 performance?

A: XML tags explicitly label each prompt component (background, task, output format) helping GPT-5 better comprehend its task, leading to dramatically improved output quality.

Q5.When should I use the perfection loop technique?

A: Use the perfection loop for complex zero-to-one tasks like creating finished documents from scratch or writing production-ready code, where GPT-5 needs to self-critique and iterate internally.

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