Configuring Model Builder is about three decisions: which Salesforce-hosted models to enable, which Trust Layer third-party models to connect, and whether to set up any bring-your-own LLM connections. Most orgs start with defaults; customers with specific cost, quality, or compliance needs configure more deliberately. Plan the model strategy with your account executive because licensing varies by model source.
- Identify model needs based on use cases
List the use cases the org plans to support. Sales email drafting, service response generation, agent conversations, document summarization. Each may need a different model based on cost, quality, and latency requirements.
- Enable Salesforce-hosted models
Setup > Einstein Setup > Generative AI > enable. Pick which Salesforce-hosted models to make available. xGen is the default; other Salesforce-hosted models may be available based on edition and add-ons.
- Connect Trust Layer third-party models
Setup > Model Builder > Add Connection. Pick the provider (OpenAI, Anthropic, Google, AWS Bedrock). Provide API credentials and configure the model endpoint. Salesforce applies the Trust Layer protections automatically.
- Configure bring-your-own LLM if needed
For custom hosted models, set up the BYO LLM connection. Provide the endpoint URL, authentication, and request/response format. Validate the connection by running a test prompt through the model.
- Assign models to prompt templates
Open each Prompt Template in Prompt Builder. Pick the model that fits the use case. Document the rationale; future admins need to know why each template uses its specific model.
- Monitor usage and quality
Setup > Einstein Generative AI > Usage. Review per-model token counts, response times, error rates, credit consumption. Look for templates whose model choice produces poor quality or excessive cost; adjust accordingly.
- Set credit consumption alerts
Configure alerts for credit spend approaching the entitlement. High-volume templates with expensive models can produce significant credit consumption; alerts let you intervene before overage charges.
- Review and rotate models periodically
LLM capabilities improve rapidly. Newer model versions often outperform older ones at lower cost. Schedule quarterly reviews of model choices and rotate where the trade-offs have shifted.
Salesforce-hosted, Trust Layer third-party, or bring-your-own LLM. Different cost, quality, and compliance profiles.
Data masking, retention policy, and audit settings that apply to all LLM interactions through the platform.
Each Prompt Template specifies which model it uses. Allows different templates to use different models based on use case.
- Third-party LLM credits consume per token. High-volume templates with expensive models like GPT-4 produce significant cost. Monitor consumption closely.
- Model quality varies significantly. Defaulting every template to the most powerful model wastes credits; matching model to use case optimizes cost without sacrificing quality.
- BYO LLM connections need careful authentication and rate-limiting configuration. Misconfigured endpoints can leak data or fail unpredictably under load.
- LLM capabilities evolve rapidly. The optimal model today may be outclassed by a newer release in six months. Schedule periodic reviews to stay current.
- The Einstein Trust Layer matters for compliance. Confirm your model connections route through the Trust Layer; direct API calls bypass the protections.