Integrating LLMs into enterprise applications requires careful architectural planning. The wrong approach leads to unpredictable costs, security risks, and unreliable outputs.
The Gateway Pattern
Route all LLM calls through a central gateway that handles authentication, rate limiting, cost tracking, and response caching. This gives you a single point of control and observability for all AI interactions across your organization.
RAG Architecture
Retrieval-Augmented Generation combines your proprietary data with LLM capabilities. Build a vector database of your documentation, knowledge base, and internal data. Retrieve relevant context before generating responses to ensure accuracy and reduce hallucinations.
Prompt Management
Treat prompts as code. Version them, test them, and deploy them through your CI/CD pipeline. Use prompt templates with variable injection rather than string concatenation to maintain consistency and enable A/B testing.
Safety and Guardrails
Implement input validation, output filtering, and content moderation. Define clear boundaries for what the LLM can and cannot do. Log all interactions for audit trails and continuous improvement.
Enterprise LLM integration is about building reliable, secure, and cost-effective AI capabilities—not just making API calls.