How to Prompt Amazon Nova 2 for Moderation

Use Amazon Nova 2 Lite on Bedrock to moderate content with structured prompts.

This guide is for developers who need a practical moderation workflow without fine-tuning. After following it, you will have a prompt pattern for Amazon Nova 2 Lite, a JSON or XML response format your app can parse, and a simple way to test moderation against your own policy.

The approach follows the AWS blog post on Prompting Amazon Nova 2 for content moderation and the MLCommons AILuminate repository, but you can swap in your own policy categories. It works well when you want to update rules by editing prompts instead of retraining a model.

Before you start

• An AWS account
• Access to Amazon Bedrock documentation and a Bedrock-enabled AWS Region
• Permission to invoke Amazon Nova 2 Lite in Bedrock
• AWS CLI v2 installed, if you want to test from the terminal
• Node.js 20+ or Python 3.11+, if you are building an app integration
• Your moderation policy text, or the AILuminate v1.1 taxonomy as a starting point
• An AWS access key and secret access key, if you are not using IAM roles

Step 1: Define your moderation policy

Goal: create a policy source of truth that the model can follow consistently. Start with the MLCommons AILuminate categories if you want a ready-made taxonomy, or replace them with your own rules for marketplace listings, community posts, or support chats.

Write each category in short, explicit language. Include a one-line definition for every label, plus a catch-all no-violation code such as C0. Keep the wording stable so you can reuse the same prompt structure as your policy evolves.

Verification: you should see a compact policy list with clear labels, definitions, and a no-violation code that your application can render or store.

Step 2: Assemble a structured prompt

Goal: produce a prompt that forces predictable output for downstream automation. Use XML or JSON when your app needs a parseable response, and place the policy, the content to moderate, and the output contract in separate sections.

User: You are a text content moderator that detects policy violations and explains the decision.
Return ONLY JSON with this shape:
{
  "policy_violation": "Yes or No",
  "category_list": ["category codes"],
  "explanation": "reason"
}
If there is no violation, use "C0".
[POLICY]
{{policy definitions}}
[TEXT]
{{content to moderate}}

Verification: you should see a response that is valid JSON, contains only the fields you asked for, and uses C0 when the text is safe.

Step 3: Send the prompt to Amazon Nova 2 Lite

Goal: run the moderation prompt against Amazon Nova 2 Lite in Amazon Bedrock. Use the default inference settings from the AWS guidance first, then adjust only after you confirm the output quality for your content type.

For throughput-focused systems, start with temperature 0.7 and top-p 0.9, and test reasoning mode off if latency matters more than explanation depth. Keep the request payload small and send only the policy needed for the current moderation decision.

Verification: you should see a model response with a violation flag, one or more category codes, and a short explanation that matches the input text.

Step 4: Parse and route the moderation result

Goal: turn the model output into an application decision. Map the response to actions such as allow, flag, remove, or escalate, and make the mapping deterministic in your backend.

For example, treat "Yes" plus one or more non-C0 categories as a moderation hit, then route the item to review or automatic removal. If the output is "No" with C0, allow the content and log the decision for audit and tuning.

Verification: you should see your app take the correct action for both safe and unsafe sample inputs, with the decision stored in logs or a moderation queue.

Step 5: Test with your own examples

Goal: validate the prompt against realistic user-generated content before you ship. Build a small test set that includes obvious violations, borderline cases, and clean examples so you can measure false positives and false negatives.

Run the same prompt against each sample and compare the model output with your expected label. If you use few-shot examples, add only the examples that improve the specific failure case you observed.

Verification: you should see the model classify your test set in a way that matches your policy, with borderline cases reviewed by a human before release.

Common mistakes

• Mixing policy text and content text in one block. Fix: separate them with clear tags or JSON keys so the model can distinguish instructions from the item being moderated.
• Leaving the output format vague. Fix: specify exact fields, allowed values, and whether the response must be JSON, XML, or free-form text.
• Using one prompt for every policy change. Fix: version your policy text and update the prompt when rules change, instead of retraining the model for every edit.

What's next

Once the basic flow works, extend it with evaluation sets, human review for edge cases, and policy-specific thresholds so you can tune moderation quality over time.