OpenAI Quietly Told You to Throw Away Your Prompt Stack

2026-05-12 · Source: AI Advances - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

OpenAI and Anthropic have independently released updated prompting guidance for their latest large language models, GPT-5.5 and Claude Opus 4.7, respectively. OpenAI's April 24 guidance, found in its API documentation, advises users to define target outcomes and success criteria while reducing or removing detailed step-by-step process guidance, allowing the model to determine its own path. Similarly, Anthropic's updated documentation for Claude Opus 4.7, released a week prior, emphasizes describing the desired outcome and letting Claude figure out the implementation. Their CLAUDE.md guide further recommends ruthlessly pruning instructions that the model already executes correctly without explicit direction. Both companies, despite operating independently with different architectures, arrived at a consistent conclusion regarding optimal prompting strategies.

Key takeaway

For prompt engineers optimizing LLM interactions, you should immediately review and refactor your existing prompt stacks. Focus on clearly defining the desired outcome and success criteria for GPT-5.5 and Claude Opus 4.7, rather than providing detailed, step-by-step process guidance. This approach allows the models to leverage their advanced capabilities more effectively, potentially improving output quality and reducing prompt complexity. Experiment with removing redundant instructions to streamline your prompts.

Key insights

Modern LLMs perform best when given clear outcomes, not prescriptive step-by-step instructions.

Principles

• Define target outcomes and success criteria.
• Allow the model to determine its own path.
• Ruthlessly prune unnecessary instructions.

Method

The method involves shifting from detailed procedural prompting to outcome-oriented prompting, where the user specifies the desired result and success metrics, then removes any instructions the model already handles.

In practice

• Refactor existing prompts to focus on desired outputs.
• Test prompts by removing step-by-step guidance.
• Iteratively remove redundant instructions.

Topics

• Prompt Engineering
• OpenAI GPT-5.5
• Anthropic Claude Opus 4.7
• Large Language Models
• Prompting Guidance

Best for: Prompt Engineer, AI Engineer, Machine Learning Engineer

Related on AIssential

• OpenAI says old prompts are holding GPT-5.5 back and developers need a fresh baseline — GPT-5.5 performs best with minimal, outcome-focused prompts, requiring a fresh approach to prompt engineering.
• 5 Prompts That Get Dramatically Better Answers Out of Claude — Specific, short prompts override LLM defaults to yield superior, tailored results.
• When Gradients Collide: Failure Modes of Multi-Objective Prompt Optimization for LLM Judges — Multi-objective prompt optimization for LLM judges fails due to gradient dilution and instruction interference.
• You Might Want to Re-Learn Prompting... — ChatGPT 3.5 models perform better with outcome-first, shorter prompts that define the desired result.
• llm 0.32a2 — OpenAI's GPT-5 class models now use `/v1/responses` for interleaved reasoning, enhancing tool call visibility.
• Anthropic and OpenAI dropped their coding models 20 minutes apart. This rivalry is getting wild — AI model developers are engaging in aggressive, simultaneous product releases and competitive tactics.

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Advances - Medium.