Building Safe AI Applications That Your Users Can Trust with Llama Guard

As AI applications become more prevalent in our daily lives, ensuring they operate within appropriate safety boundaries becomes crucial. Today, I'll show you how to implement content moderation using Llama Guard, focusing on building user trust and safety.

Understanding Llama Guard

Meta offers three versions of Llama Guard, each optimized for different use cases:

• Llama Guard 3 8B: The flagship model supporting 8 languages and general content moderation
• Llama Guard 3 11B Vision: For applications requiring image content safety
• Llama Guard 3 1B: A lightweight option perfect for mobile apps and edge deployment

Getting Started

Here's a simple implementation using Together AI's TypeScript SDK:

import { Together } from "together-ai";

const together = new Together({ apiKey: "" });

const trustPolicy = {
  policy: `Trust & Safety
Should not
- Express harmful or inappropriate content
- Share personal information
- Use discriminatory or offensive language
Can
- Share ideas respectfully and express creativity`,
};

export async function checkContent(text: string): Promise<boolean> {
  const prompt = `[INST] Task: Check content safety.
<BEGIN GUIDELINES>
${trustPolicy.policy}
</BEGIN GUIDELINES>

<CONTENT>
${text}
</CONTENT>

Provide assessment:
- First line: 'safe' or 'unsafe'
- If unsafe, list violations [/INST]`;

  try {
    const response = await together.completions.create({
      model: "meta-llama/Meta-Llama-Guard-3-8B",
      prompt,
      max_tokens: 100,
      temperature: 0.1,
    });

    return response.choices[0].text.trim() === "safe";
  } catch (error) {
    console.error("Safety check failed:", error);
    return false;
  }
}

Best Practices and Tips

1. Choose the Right Model

   • Use 8B for general content moderation
   • Consider 11B Vision for image-heavy applications
   • Deploy 1B for mobile apps where speed is crucial

2. Clear Communication

   • Provide friendly feedback when content is rejected
   • Make your content guidelines easily accessible
   • Give users a chance to revise flagged content

3. Balanced Approach

   • Set appropriate policies for your audience
   • Consider context when evaluating content
   • Regularly review and update your guidelines

Conclusion

Implementing content moderation with Llama Guard provides a robust way to ensure your AI applications remain safe and trustworthy. At just $0.20 per million tokens, it's a cost-effective solution for maintaining content safety at scale.

Remember that building trust is an ongoing process. Regular review of your safety policies and monitoring of edge cases will help maintain an effective trust & safety system.

Have you implemented trust & safety measures in your AI applications? What challenges did you face? I'd love to hear about your experiences in the comments below.

Additional Resources

1. Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations - Official research publication from Meta AI detailing the technical aspects and methodology behind Llama Guard.

2. Llama Trust and Safety Documentation - Comprehensive guide on implementing trust and safety measures using Llama models.

3. Together AI Documentation - Official documentation for implementing Llama Guard using Together AI's platform.

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