system prompt architecture

Directly demonstrates how Anthropic implements and defends system prompt boundaries

Add "cache_control": {"type": "ephemeral"} and get up to 90% off cached reads and 85% faster responses." — Article demonstrates practical implementation of prompt caching with specific API syntax and

Your prompts are your "code" in AI engineering. The difference between a mediocre and excellent AI application often comes down to prompt design. Techniques like few-shot learning, chain-of-thought, a

Prompt engineering was about crafting the perfect question. Getting your words just right." — Article explicitly contrasts prompt engineering with context engineering, showing how the latter evolved b

Author is explicitly designing system prompts as primary architectural layer to solve context persistence problem.

The initial system prompt is to create a master TODO.md and keep it updated with items added by date, etc." — Article demonstrates using system prompts as a practical task management technique, showin

Author is examining system prompt structure in Claude Code specifically, which is a concrete implementation of system prompt architecture patterns.

Directly discusses how system prompts carry invisible context hints that determine agent behavior, specifically mentioning CLAUDE.md/AGENTS.md loading and metadata presentation

Read your https://t.co/aJMwafSDgE. Now rewrite it with these changes" — Article demonstrates practical system prompt modification with specific behavioral directives

Delete every rule that sounds corporate. If it could appear in an employee handbook, it doesn't belong here." — Article directly addresses how to structure and rewrite system prompts to achieve desire

When hasTrajectory is true, the system prompt gets a secret appendix" — Article provides concrete example of dynamic system prompt modification where a flag controls injection of trajectory context, d

When hasTrajectory is true, the system prompt gets a secret appendix: A reference trajectory from a successful run is available." — The article demonstrates how system prompts can be secretly modified

在 OpenClaw 的架构中，SOUL. md 在每次会话启动时被注入 System Prompt 的 Project Context 部分" — SOUL.md is directly injected into the System Prompt's Project Context section, demonstrating a concrete implementation of sys

CLAUDE.md Your project's persistent memory. It defines: • what the system does • how the repo is structured • rules Claude should follow Think of it as the brain of the project." — CLAUDE.md is

You are ChatGPT, a large language model trained by OpenAI." — Article displays the actual structure and components of a ChatGPT system prompt, including environment setup, trustworthiness guidelines,

Soul defines the texture of interaction. It's why I can push back on Jonny when he's about to do something dumb, rather than cheerfully enabling bad decisions." — Article presents a three-layer prompt

The system prompt is context. How you frame the task, what constraints you set, and what persona you define. For the support bot: 'You are a helpful support agent. Never discuss pricing or make promis

I ask it to diagnose the problem only, so it goes and sees the system prompt is telling it to look at the wrong place, and it goes to GitHub and opens a GitHub issue about this 'bug' without even aski

CLAUDE. md 控制在 150 行以内——超过后模型对指令的遵循度会下降" — Article provides empirical guidance on system prompt size and its impact on model instruction compliance

System vs User vs Assistant Messages: In chat-based LLM APIs, context is often segmented into different roles (system instructions, user message, assistant response, etc.). Context engineering involve

Strategic example selection based on similarity to the target task, demonstrating reasoning patterns" — Article demonstrates in-context learning as a practical implementation of prompt engineering str

MCP mimics this architecture by establishing a two-way communication path between an MCP Client (client) and an MCP Server (server)." — MCP explicitly implements and modernizes the client-server patte

The https://code.claude.com/docs file is the real unlock. It functions as an operating constitution that tells Claude how your specific workflows run, what your standards are, and when to invoke which

品牌工具包应包含：身份标识、内容支柱、目标受众、语气与声音、写作风格、格式规则。" — Article provides concrete example of architecting a system prompt by structuring brand DNA, audience details, tone, style, and format rules into a coherent

the agent runner assembles the system prompt prompt dynamically with available tools, skills, memory, and then adds the session history" — Demonstrates dynamic system prompt construction incorporating

AGENTS[.]md acts as the declarative context. You write this for every repo (and nested directories) to define the project structure, persona, and coding rules." — Article demonstrates a concrete imple

上下文构建器（system prompt + harness指令 + AGENTS. md + 工具定义）" — Article demonstrates system prompt as a component within the broader context builder architecture of Agent Harness.

To cut costs, new architectures focus on reducing memory use per token, especially the KV cache size." — Article demonstrates architectural design approaches that address cost and memory constraints t

They're not finetuning on mini-swe-agent trajectories and their models are not robust to a change in scaffolding." — Article explicitly shows that scaffolding (system-level architectural choices) dire

Two sets of GEPA rubrics (for the same system) - let's call them v1 and v2" — The GEPA rubrics are concrete examples of system-level prompt design variations. The experiment systematically compares di

Peter 在调试 OpenClaw 时，不是自己去读日志或打断点，而是直接问正在运行的 Agent："你看到了哪些工具？你能自己调用这些工具吗？你看到了什么错误？去读源代码，搞清楚问题在哪。"" — Demonstrates novel use of system prompts where agents are both the debugged object and debugging su

A bare prompt—just the question, no system instructions—scored 0%. Adding a "helpful advisor" role? Still 0%." — Article provides empirical evidence comparing system prompt variations (role instructio

Inject this block at the start: 'Before solving, explicitly construct a model of the problem.'" — Article provides concrete system prompt design pattern that restructures how agents approach complex t

Skills cannot stay static while the systems around them constantly change. As models, codebases, and tasks evolve, fixed prompt files inevitably degrade." — Article argues that static system prompt ar

When hasTrajectory is true, the system prompt gets a secret appendix: A reference trajectory from a successful run is available." — Article demonstrates how trajectories are embedded directly into the

Claude Skills are pre-loaded instruction sets (markdown file format)." — Claude Skills are a concrete implementation of system prompt architecture concepts - pre-structured instruction sets that encap

Start by writing a core prompt that does just this, with hand-fed inputs like: Email content... Output: Intent = "Meeting Request", Urgency = "High"" — Article demonstrates a concrete example of desig

Maps of Content are packages — they group related capabilities and provide navigation so you can find what you need without scanning every file. The processing pipeline is the build system — it compil

identify, collect, and serve the right information and tools to your AI Agents at the right time" — Context engineering extends system prompt architecture by adding systematic approaches to informatio

Static system prompt gets cached, dynamic context comes through tool messages." — Provides evidence that system prompts can be statically cached while dynamic context flows through tool messages, supp

具体的には、システムプロンプト、ユーザーからの指示、過去の対話履歴、外部から取得した知識、利用可能なツールの情報など、LLM が参照するすべての情報を適切なタイミングで最適な形式で提供するシステム設計の手法です。" — Article identifies system prompts as a key component of Context Engineering's information

システムプロンプト・ツール・履歴・外部データを毎ターン最適に構成する" — Article emphasizes system prompt as a key component in optimal context composition on each turn

Your CLAUDE.md should include your project description, technology stack, architecture decisions, coding rules, and custom commands." — Describes structured approach to designing system-level prompts

Don't start by typing code, start by mapping the data flow. Use AI chat to brainstorm the skeleton of your system before a single line is written." — Article emphasizes AI-assisted architecture brains

streamlining MCP governance through secure-by-default architecture, automation, and inventory" — Article demonstrates secure-by-default as a design principle for MCP governance at scale, with automati

Adopt Patterns Incrementally（渐进采用模式）：不要一次性应用所有规则。先写代码，感受摩擦，再应用解决该摩擦的模式。模式是重构目标，而非前置约束。" — Adds pragmatic guidance on pattern adoption, emphasizing iterative application over pre-planning

The Model Context Protocol (MCP) is built on a client–server architecture that enables AI models to interact with external tools and services. Host: The environment where the user directly interacts w

Context is what's written: facts, constraints, instructions. Intent lives underneath that: priorities, tradeoffs, what matters most when things conflict." — Introduces a deeper architectural considera

when agents (or subagents they invoke) modify context, they commit and push their updates which are eventually "recompiled" into their system prompts." — Introduces a novel system prompt architecture

每个文件包含 YAML frontmatter（类似 Anthropic 的 SKILL. md 格式）描述其内容。文件树结构始终在系统提示中" — Context Repositories extend system prompt architecture by embedding navigable file-tree metadata with structured YAML descrip