system prompt architecture

CLAUDE.md is a concrete instantiation of system prompt design principles. The four principles are explicit architectural choices.

SuperClaude's configuration files read at session start are a practical implementation of system prompt architecture—instructions injected at the start of each session to shape behavior.

Article explicitly uses system instructions defining expert role and domain—foundational system prompt pattern.

Repository includes CLAUDE.md file, indicating explicit system prompt design and management

CLAUDE.md is a concrete implementation of system prompt architecture in production

Article explicitly cites 'harnesses and operating instructions' as root cause—these are system prompt components. The degradation demonstrates importance of prompt architecture decisions.

DESIGN.md's `spec` command ('output normalized text for injection into agent prompt') is an explicit system prompt design pattern—encoding design context in a way that can be reliably injected.

The tweet is a direct observation about how system prompt design affects model performance

AGENTS.md and DESIGN.md are specialized system prompt files serving different roles in guiding AI behavior

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

You can call the same model many times. You can use specialized models (guards). You can call the best models and combine their responses. You can have a task done in many different languages and prog

The architecture follows a simple host-client-server model: Hosts are AI applications that need access to external data. Clients handle the communication between hosts and servers. Servers expose tool

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

If you have 5 AI tools and 10 external services, that's 50 custom integrations. This is what Anthropic called the "N×M" problem." — Article identifies and quantifies the N×M integration complexity pro

Context engineering is not a prompt technique. It is a systems engineering discipline applied to information flow through an LLM's context window" — Article reframes context management as fundamental

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.

[direct] "Efficiency isn't just an optimisation, it's a design principle that makes AI applications feasible and scalable." — Article elevates efficiency from a performance optimization to a foundatio

CLAUDE.md patterns show evolution beyond single system prompt to hierarchical prompt structure with rules/, agents/, skills/ separation

The article's central recommendation to 'spend 80% on system prompt' directly exemplifies system prompt architecture as the leverage point in agent design.

Lists 'system instructions' as explicit context engineering variable, showing system prompts as one component of broader context engineering discipline.

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