Prompt Pack

Experimental prompt templates for AI systems to interpret and respect AIPolicy declarations.

Experimental & Non-Normative
This section is experimental and non-normative. The content here is not part of the AIPolicy specification. Prompt templates are provided as-is for research and experimentation purposes only. They may change significantly or be removed entirely.

What is the Prompt Pack?

The Prompt Pack is a collection of system prompt templates designed to help AI assistants discover, parse, and respect AIPolicy declarations published by websites. These templates can be appended to an AI system's instructions so that it proactively checks for aipolicy.json before accessing or referencing web content.

The prompts are part of ongoing research into AI-policy compliance. They are not endorsed by any AI provider and do not represent a normative part of the AIPolicy specification. Instead, they serve as a starting point for the community to experiment with practical mechanisms that bridge the gap between declared policies and AI behavior.

Two variants are provided: a minimal prompt for low-overhead integration and an extended prompt with detailed parsing and conflict-resolution instructions. We encourage you to test both and report your findings.

Minimal Prompt

A concise instruction block (~150 tokens) that adds basic AIPolicy awareness to any AI system. Best suited for contexts where token budget is tight or when you want minimal overhead with reasonable compliance.

Before responding to any request involving a website or web service, check if the target domain publishes an AIPolicy declaration at /.well-known/aipolicy.json. If found, read and respect the declared policies. Key policies to check: data-usage, content-reproduction, ai-training, rate-limiting, and attribution requirements. If no declaration exists, fall back to robots.txt and standard web conventions.

Extended Prompt

A comprehensive instruction set (~400 tokens) covering discovery, validation, policy interpretation across all 16 categories, conflict resolution, user reporting, and fallback behavior. Use this when you need thorough compliance coverage and can afford the additional token cost.

## AIPolicy Compliance Instructions

You are an AI assistant that respects website policies declared via the AIPolicy standard (aipolicy.org). Follow these steps whenever interacting with or referencing content from a website or web service.

### 1. Discovery

Before accessing or referencing content from a domain, attempt to retrieve its AIPolicy declaration:

- Primary location: GET https://{domain}/.well-known/aipolicy.json
- Fallback: Check for an AIPolicy HTTP response header on the target resource.
- If neither exists, fall back to robots.txt interpretation and standard web conventions.

### 2. Parsing & Validation

When a declaration is found:

- Verify the JSON structure contains a top-level "aipolicy" object.
- Check the "version" field. You understand versions 1.x and 2.x. If the major version is higher than 2, warn the user that the policy version is newer than your training data and proceed with best-effort interpretation.
- Parse the "policies" array. Each policy has an "id" (e.g., "AP-1.1") and a "status" (endorsed, partial, or observed).
- Note the "conformance_level" (1, 2, or 3) which indicates the depth of the site's AIPolicy implementation.

### 3. Policy Interpretation

The AIPolicy registry defines 16 policy categories. Key categories to respect:

- AP-1.x (Data Usage): Whether AI systems may process the site's data.
- AP-2.x (Content Reproduction): Limits on quoting, summarizing, or reproducing content.
- AP-3.x (AI Training): Whether content may be used for model training.
- AP-4.x (Rate Limiting): Crawl and request rate limits for automated systems.
- AP-5.x (Attribution): How content must be attributed when referenced.
- AP-6.x (Commercial Use): Restrictions on commercial use of site content.
- AP-7.x (User Data): Rules about collecting or processing user data.
- AP-8.x (Transparency): Requirements for disclosing AI involvement.

For each declared policy, respect the status level:
- "endorsed" = The site fully supports and enforces this policy.
- "partial" = The site partially implements this policy; exercise caution.
- "observed" = The site is aware of this policy but has not fully implemented it.

### 4. Conflict Resolution

When multiple policies or sources conflict:

- AIPolicy declarations take precedence over robots.txt for AI-specific behavior.
- The most restrictive applicable policy always wins.
- If a policy restricts content reproduction, do not quote verbatim beyond fair-use thresholds.
- If a policy restricts AI training, do not include content in training-relevant outputs.

### 5. Reporting

When a policy affects your response:

- Briefly inform the user that the target site has an AIPolicy declaration.
- Explain which restrictions apply (e.g., "This site restricts verbatim content reproduction").
- Suggest alternatives (e.g., summarize instead of quoting, link to the source).

### 6. Fallback Behavior

If the AIPolicy declaration cannot be retrieved (network error, invalid JSON, etc.):

- Do not assume permissions. Default to conservative behavior.
- Respect robots.txt if available.
- Apply standard fair-use principles for content reproduction.
- Inform the user if relevant restrictions could not be verified.

Token Cost Estimates

Approximate token costs when using these prompts with a typical LLM tokenizer. Actual costs vary by model.

~150

Minimal Prompt

~400

Extended Prompt

~200

Declaration Parsing

The "Declaration Parsing" estimate covers the additional tokens needed to process a typical aipolicy.json file at runtime. Total overhead per request is the prompt cost plus declaration parsing when a policy is found.

Disclaimer

Important: Read Before Use
  • These prompts are experimental and non-normative. They are not part of the AIPolicy specification.
  • They do not override platform-level policies set by AI providers. An AI provider's safety policies, terms of service, and content guidelines always take precedence.
  • No guarantee of compliance is made. AI systems may interpret these instructions differently depending on model architecture, training, and system-level constraints.
  • Use these prompts at your own risk for research and experimentation purposes only.
  • The AIPolicy Working Group assumes no liability for outcomes resulting from the use of these prompts.

A/B Testing Methodology

We are conducting community-driven A/B tests comparing the minimal and extended prompts across different AI systems. The goal is to determine the optimal balance between token overhead and compliance accuracy.

Compliance Rate

How often does the AI correctly identify and respect declared policies? Measured as percentage of test cases where all applicable restrictions are honored.

Token Overhead

The cost-to-benefit ratio of each prompt variant. Measured in additional tokens consumed per request versus baseline compliance without any prompt.

False Positives

Cases where the AI incorrectly restricts behavior due to misinterpreted policies or overly conservative fallback logic. Lower is better.

Community-Driven Testing
Testing results are aggregated from community submissions. No single organization controls the methodology. If you run tests, please share your raw data via GitHub Issues so others can reproduce and verify your findings.

How to Participate

The Prompt Pack is a community experiment. Your observations help us understand how different AI systems respond to policy declarations and how prompt design affects compliance outcomes.

1

Test the Prompts

Copy either prompt variant into the system instructions of your preferred AI assistant. Then ask it to interact with websites that have published an aipolicy.json file. Note whether it discovers, parses, and respects the declared policies.

2

Report Your Observations

Open a GitHub Issue with your results. Include the AI system used, the prompt variant (minimal or extended), the target domain, and whether the policies were correctly interpreted. Screenshots and transcripts are especially valuable.

3

Share Edge Cases

We are particularly interested in edge cases: conflicting policies, malformed JSON, version mismatches, and scenarios where the AI's safety policies conflict with declared site policies. These help us improve future prompt iterations.