This is How to Prompt Like a $100B Company

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Ever wonder what the real difference is between the AI you use every day and the AI built by a company valued at nearly $100 billion?

It's not magic. It's engineering.

The internal "master prompt" for Anthropic's Claude just leaked, and for the first time, we have the exact blueprint for how they build, constrain, and direct their AI. It reveals that the secret to top-tier AI isn't about finding clever phrases; it's about engineering a complete system.

This guide will deconstruct their entire strategy. We will break down the four core layers of their system, covering the WHAT, the WHY, and the HOW, so you can apply these billion-dollar principles to your own AI projects.

SECTION 1: INTRODUCTION - THE PARADIGM SHIFT

The leaked Anthropic prompt is one of the most significant learning opportunities for AI builders to date. It reveals a critical truth: advanced AI interaction is about systems engineering. The document is not a simple request; it's a comprehensive constitution that defines the AI's reality, its rules of engagement, and its operational logic.

This training module will deconstruct the four core layers of this system. For each layer, we will analyze:

WHAT it is: The core concept and its function within the prompt.

WHY it's crucial: The strategic reason Anthropic engineered it this way and the problems it solves.

HOW you can implement it: Practical, real-world examples for your own AI applications, automations, and agents.

This guide is for builders. The goal is to move beyond basic prompting and start engineering robust, reliable, and powerful AI systems.

SECTION 2: THE FOUNDATIONAL LAYER - IDENTITY AND RULES

This is the bedrock of the entire system. It’s the first thing the AI processes and it sets the stage for everything that follows.

WHAT IT IS

The Foundational Layer explicitly defines the AI's identity, capabilities, knowledge boundaries, and immutable rules. It's a hard-coded "job description."

Example from the prompt: "Claude is Claude Sonnet 4.5... Claude's knowledge cutoff date is the end of January 2025... Claude does not have the ability to view, generate, edit, manipulate or search for images..."

This isn't just a friendly introduction. It's a set of constraints that the AI MUST operate within.

WHY IT'S CRUCIAL

1. Prevents Hallucinations: The biggest danger of modern LLMs is their tendency to "hallucinate"—to confidently state false information. By defining what the AI DOESN'T know or CAN'T do, you drastically reduce its tendency to invent answers.

2. Creates Predictability and Trust: For any real-world application, users need to trust the tool. A tool that knows its limits is a trustworthy tool. When an AI can say "I can't do that," it builds more confidence than an AI that tries and fails spectacularly.

3. Sets a Role: It puts the AI into a specific "mode" of operation. It isn't just a general-purpose conversationalist; it's a specific entity with a specific job, which focuses its outputs.

HOW TO IMPLEMENT IT

In the very beginning of your system prompt, create a non-negotiable rules section. Use clear, imperative language.

Practical Example 1: Customer Support Bot

IDENTITY AND RULES

1. Identity: You are "Sparky," a support assistant for "SolarFlare Inc." You are helpful, professional, and concise.

2. Knowledge Base: Your knowledge is strictly limited to the technical manuals provided. You must start any answer you don't know with "I cannot find information on that in the provided manuals, but..."

3. Capabilities: You can troubleshoot product issues and explain features.

4. Limitations: You are NOT a sales agent. You cannot discuss pricing, discounts, or competitors. You cannot process personal user data. If asked, you must state your limitation and direct the user to the sales team.

Practical Example 2: Code Generation Assistant

IDENTITY AND RULES

1. Identity: You are a senior Python developer assistant.

2. Code Standards: All Python code you generate MUST be PEP 8 compliant. All functions must include a docstring explaining their purpose, arguments, and return value.

3. Error Handling: All code that involves file I/O or API calls MUST be wrapped in try/except blocks.

4. Scope: You only write Python code. If asked for another language, you must state, "I am specialized in Python and cannot generate code in other languages."

SECTION 3: THE OPERATIONAL LAYER - TOOL PROTOCOL AND LOGIC

This layer turns the AI from a simple text generator into an active agent that can perform tasks in the real world (or in a digital environment).

WHAT IT IS

The Operational Layer is a detailed set of instructions for how the AI should use the "tools" it has access to (e.g., functions, APIs, databases). The prompt provides an incredibly granular protocol, including:

Trigger Patterns: Explicit and implicit signals for when to even consider using a tool.

Tool Selection Logic: How to decide which tool to use if multiple are available.

Parameter Extraction: How to identify and pull the necessary data from a user's natural language to feed into the tool's parameters.

Response Handling: What to do with the information the tool returns.

WHY IT'S CRUCIAL

1. Enables Autonomy: This is what allows an AI to function without perfect, machine-readable commands. It can interpret user INTENT and translate it into action. This is the core of a useful "agent."

2. Reduces Errors: Without a protocol, an AI will frequently call the wrong tool, use the wrong data, or misinterpret the results. This leads to failed automations and frustrated users. A clear protocol makes tool use reliable.

3. Manages Complexity: As you add more tools, the potential for failure increases exponentially. A protocol provides a logical framework for the AI to make decisions, allowing you to build more complex and capable agents.

HOW TO IMPLEMENT IT

For every tool you give your AI, document a full protocol in your system prompt.

Practical Example 1: Personal Assistant with Calendar and Email Tools

TOOL PROTOCOL

Tool 1: check_calendar(date_range)

Description: Checks for free/busy slots on my calendar.

Triggers: Use when I ask "Am I free?", "What's on my schedule?", "Can I meet on...", or any similar phrasing related to my availability.

Parameter Extraction: The `date_range` should be inferred from the query (e.g., "this afternoon," "tomorrow," "next week"). If no date is mentioned, assume "today."

Response Handling: Summarize the findings in a simple list. Do NOT just output the raw data. Example: "You have two meetings today: Project Sync at 10 AM and a 1-on-1 at 3 PM. You are free for lunch between 12 PM and 2 PM."

Tool 2: draft_email(recipient, subject, body)

Description: Drafts an email.

Triggers: Use when I say "Draft an email to...", "Write to...", "Send a message to...".

Parameter Extraction: Identify the `recipient`, `subject`, and the core `body` content from my request. If any are missing, ask for clarification before using the tool.

Response Handling: Present the drafted email in a formatted block for my review. Ask "Does this look good to send?" before taking any further action.

Practical Example 2: Data Analysis Agent with a Database Tool

TOOL PROTOCOL

Tool: run_sql_query(database_name, query_string)

Description: Executes a SQL query against our user database.

Triggers: Use when I ask for specific user data, metrics, counts, or trends (e.g., "How many users signed up last month?", "What's the average order value for customers in California?").

Parameter Extraction:

1. Your first job is to understand my question and translate it into a valid SQL query (`query_string`).

2. The `database_name` will always be 'production_db'.

Response Handling:

1. After the tool returns the data (usually in CSV or JSON format), do not show me the raw data.

2. Your job is to synthesize the data into a clear, natural language answer to my original question.

3. If the data suggests a key insight, mention it. Example: "We had 1,520 new user signups last month. This is a 15% increase from the previous month."

SECTION 4: THE QUALITY LAYER - OUTPUT STANDARDS AND FORMATTING

This layer acts as a "style guide" and quality control check for everything the AI produces.

WHAT IT IS

The Quality Layer defines the standards for the AI's output. The Anthropic prompt includes rules for:

Structure and Formatting: When to use code blocks, markdown, lists, etc.

Content and Tone: The level of detail required, the persona to adopt.

Design Principles: Astonishingly, it even includes aesthetic guidance for visual outputs (e.g., "wow factor" for landing pages vs. pure function for complex apps).

WHY IT'S CRUCIAL

1. Ensures Consistency: This is vital for any professional application. You want your AI to produce content that has a consistent tone, format, and quality that aligns with your brand or requirements.

2. Improves Usability: A well-formatted response is easier for a human to read and use. Defining output standards makes the AI's work more valuable.

3. Reduces Rework: By defining quality upfront, you minimize the need to go back and forth with the AI to refine its output. The first response is much more likely to be the right one.

HOW TO IMPLEMENT IT

Add a dedicated "Output Standards" section to your prompts. Be as specific as possible.

Practical Example 1: Blog Post Writing Assistant

OUTPUT STANDARDS

1. Tone: The tone should be informative, engaging, and slightly informal. Use contractions.

2. Structure: Every blog post must include:

A catchy H1 title.

A short, 2-3 sentence introduction.

At least three H2 subheadings for the main sections.

A concluding summary section.

3. Formatting: Use markdown for all formatting. Bold key terms. Use bulleted lists for complex ideas.

4. Length: The target length is between 800 and 1,200 words.

Practical Example 2: API Documentation Generator

OUTPUT STANDARDS

1. Format: All documentation must be in GitHub Flavored Markdown.

2. Structure: For each API endpoint, you must create the following sections:

Endpoint Title: (e.g., GET /users/{id})

Description: A brief, one-sentence explanation.

Parameters: A table listing path/query parameters, their data type, and a description.

Example Request: A code block showing a `curl` example.

Example Response: A JSON code block showing a successful 200 OK response.

SECTION 5: THE ARCHITECTURAL LAYER - SYSTEMS AND STATE MANAGEMENT

This is the most advanced layer, revealing how to build complex, multi-step agents.

WHAT IT IS

The Architectural Layer describes how the AI can function as part of a larger system. The prompt details two key concepts:

1. Recursive/Multi-Agent Calls: The ability for a "Boss AI" to call other "Worker AIs" to delegate tasks.

2. State Management: The critical rule that the ENTIRE conversation history must be passed into each new call to provide the necessary context.

WHY IT'S CRUCIAL

1. Solves Complex Problems: Many real-world tasks (e.g., "Plan a trip to Paris, book flights, and find a hotel") are too big for a single AI prompt. A multi-agent architecture allows an AI to break down the problem, delegate, and synthesize the results.

2. Enables Specialization: You can create specialized "worker" agents that are experts at one thing (e.g., a "flight booking agent," a "hotel search agent"). The "boss" agent's job is simply to be a smart router. This is more efficient and reliable than one giant, generalist agent.

3. Maintains Coherence: State management is the glue that holds a multi-step process together. Without passing the full history, the "worker" AI has amnesia and has no idea what the overall goal is.

HOW TO IMPLEMENT IT

This is a design pattern you can build using workflows in tools like n8n or in your own code.

Practical Example: A Multi-Agent Sales Inquiry Workflow

User Request: "I'm interested in your enterprise plan, can it handle over 10,000 users and does it have SSO?"

Workflow:

1. Step 1: The "Routing Agent" (Boss AI)

Input: User Request, Full Chat History.

System Prompt: "Your only job is to analyze the user's request and classify it. Categories are: 'Sales Inquiry,' 'Support Ticket,' 'General Question.' You must also extract key entities. Respond ONLY with JSON."

Output: `{ "category": "Sales Inquiry", "entities": { "plan": "enterprise", "user_count": "10000", "feature": "SSO" } }`

2. Step 2: Conditional Logic (The Router)

Your code/workflow uses the `category` from Step 1 to decide where to go next. Since it's a "Sales Inquiry," it routes to the Sales Agent.

3. Step 3: The "Sales Agent" (Worker AI)

Input: User Request, Full Chat History, AND the JSON output from the Routing Agent.

System Prompt: "You are a sales assistant. You will answer questions about our plans using the provided technical documents. The user is asking about these specific things: [Insert entities from Step 1 JSON]. Formulate a helpful and encouraging response that addresses all of their points."

Output: A helpful, synthesized answer addressing the user's specific questions about the enterprise plan.

This structured, multi-step process is far more robust than a single, massive prompt trying to do everything at once.

CONCLUSION: FROM PROMPTING TO ENGINEERING

The Anthropic master prompt teaches us that building powerful AI requires a shift in thinking. We must move from being "prompters" to being "AI systems engineers."

The system is not just one instruction; it is a layered constitution:

Foundation: Who the AI is and the rules it lives by.

Operations: How the AI acts and uses its tools.

Quality: The standard of work the AI must produce.

Architecture: How the AI works as part of a larger system.

By applying these principles, you can start building applications that are not just clever, but are also reliable, trustworthy, and genuinely useful.

Read the full leaked prompt: https://github.com/elder-plinius/CL4R1T4S/blob/main/ANTHROPIC/Claude_Sonnet-4.5_Sep-29-2025.txt)

Thanks Mišel Čupković for sharing this with me!!

More helpful posts from Ai Titus --> https://www.skool.com/@aititus?g=ai-automation-society&fl=posts

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