This article discusses how the newest generation of Claude models necessitates a shift in context engineering. Anthropic found that they could remove over 80% of their system prompts without losing performance on coding evaluations by moving away from rigid, often conflicting instructions and instead allowing the model's inherent judgment to guide its behavior based on surrounding context.
Key shifts in methodology include:
- Moving from strict rules to letting models use judgement for nuance such as documentation style or intent interpretation.
- Prioritizing intuitive tool interface design over providing restrictive examples that limit exploration.
- Implementing progressive disclosure by using skills and deferred loading to manage large context windows efficiently.
- Replacing repetitive instructions with streamlined, high-fidelity descriptions directly within tool definitions.
- Transitioning from manual memory management in files toward auto-memory and rich references like HTML artifacts or code snippets.
Key shifts in methodology include:
- Moving from strict rules to letting models use judgement for nuance such as documentation style or intent interpretation.
- Prioritizing intuitive tool interface design over providing restrictive examples that limit exploration.
- Implementing progressive disclosure by using skills and deferred loading to manage large context windows efficiently.
- Replacing repetitive instructions with streamlined, high-fidelity descriptions directly within tool definitions.
- Transitioning from manual memory management in files toward auto-memory and rich references like HTML artifacts or code snippets.
Local large language models often struggle with ambiguous prompts because they lack the massive datasets and scale used by cloud-based AI to infer user intent. To improve accuracy, users can implement a custom system prompt that instructs the model to ask up to three targeted clarifying questions before performing complex tasks like coding or writing. This approach reduces errors caused by incorrect assumptions and helps refine user instructions through active dialogue.
>"""When tasked with coding, writing, editing, or summarizing, ask the user up to three targeted clarifying questions. Proceed with the task once you've received answers and understand the prompt fully. If the task is a simple factual question or conversational message, respond directly.
"""
>"""When tasked with coding, writing, editing, or summarizing, ask the user up to three targeted clarifying questions. Proceed with the task once you've received answers and understand the prompt fully. If the task is a simple factual question or conversational message, respond directly.
"""
Unlike cloud AI services like Claude or Gemini, local LLMs lack built-in workspace features for persistent memory. You can bridge this gap using "context journaling" via system prompts and RAG.
* LM Studio presets for concise system prompts.
* RAG document uploads for background/project history.
* Markdown journal structure (Background, Projects, Corrections).
* “Corrections” section to prevent recurring model errors.
* Session exports for prompt effectiveness records.
* LM Studio presets for concise system prompts.
* RAG document uploads for background/project history.
* Markdown journal structure (Background, Projects, Corrections).
* “Corrections” section to prevent recurring model errors.
* Session exports for prompt effectiveness records.
This GitHub repository, "agentic-ai-prompt-research" by Leonxlnx, contains a collection of prompts designed for use with agentic AI systems. The repository is organized into a series of markdown files, each representing a different prompt or prompt component.
Prompts cover a range of functionalities, including system prompts, simple modes, agent coordination, cyber risk instructions, and various skills like memory management, proactive behavior, and tool usage.
The prompts are likely intended for researchers and developers exploring and experimenting with the capabilities of autonomous AI agents. The collection aims to provide a resource for building more effective and robust agentic systems.
Prompts cover a range of functionalities, including system prompts, simple modes, agent coordination, cyber risk instructions, and various skills like memory management, proactive behavior, and tool usage.
The prompts are likely intended for researchers and developers exploring and experimenting with the capabilities of autonomous AI agents. The collection aims to provide a resource for building more effective and robust agentic systems.
This file contains prompts for Claude AI, likely related to system prompt leakage.
