Week of Feb 21st
New Updates: BYO LLM
Features
Generative AI solutions: Bring your own LLM
Looking to apply brand-specific tuning to your Generative AI solution and enhance control and compliance? This message is for you.
LivePerson is thrilled to announce that, if you’ve invested in an in-house LLM, you can now use it to power the Generative AI features in your Conversational Cloud solution. This exciting new opportunity lets you align the solution with your brand’s overall LLM strategy.
Key benefits
- Enhanced customization: Use a model that’s fine-tuned by you using your brand’s data.
- Security and compliance: Configure your LLM per your security and compliance needs.
- Increased efficiency and optimized performance: Establish your own SLAs and ensure consistent performance that’s tailored to your needs.
- Cost transparency: Gain full clarity on the model’s traffic and cost.
- Flexibility (Coming soon!): Bringing your own LLM won’t mean you have to use it everywhere in your Conversational Cloud solution. If desired, you’ll soon be able to use your own in-house LLM for some Conversational AI use cases, and use the ones available via LivePerson for other use cases. This flexibility isn’t available yet, but it’s coming soon.
Supported LLMs
Currently, LivePerson recommends that you take advantage of this feature for endpoints using OpenAI GPT 3.5 Turbo in specific.
To use a different type of model, you need the ability to fully configure and customize the prompt to suit the model type. We’re working hard on adding this ability to our Prompt Library in the near future, but it’s not here yet. Stay tuned!
Onboarding
To get started, contact your LivePerson representative, as they can initiate the onboarding process. You’ll need to provide:
- Your Conversational Cloud account ID
- The path to the endpoint
- The API key
Load balancing
LivePerson’s LLM Gateway can perform client-side load balancing across resources. If you provide us with multiple endpoints, also provide your desired weight value for each endpoint.
The weight value is just an assigned number. Resources with a greater weight will be called more. So, for example, if you assign weight value "1" to resource A and weight value "2" to resource B, then in every cycle, resource A will be called 1 time, and resource B will be called 2 times. You might want to assign greater weight to freer resources.
Best practices
- Setup and onboarding: When you set up the Azure resource that you intend to share with LivePerson:
- Enable IP whitelisting to block unexpected external access, and whitelist Liveperson’s IP ranges as per this article.
- Create a dedicated API key to share with Liveperson; this API key should be used exclusively by Liveperson’s LLM Gateway.
- Share the API key in any format you desire. We recommend you do so in a manner that limits access to only those who are necessary, e.g., your LivePerson account representative.
- Load balancing: To optimize performance and scalability, configure multiple endpoints for the model and deploy them across multiple regions. LivePerson’s LLM Gateway can perform load balancing.
- Content logging: Configure content logging for your in-house LLM as you require (to facilitate model training, fine tuning, and more). Learn more about data, privacy, and security.