Design Patterns for Modern LLM Applications

Integrating LLMs into enterprise systems requires careful architectural considerations. Unlike traditional software components, LLMs are probabilistic, expensive to run, and can produce unexpected outputs.

The RAG Pattern

Retrieval-Augmented Generation (RAG) has emerged as the standard pattern for grounding LLM responses in enterprise knowledge. Instead of asking the model to recall facts from training data, RAG retrieves relevant documents from a vector database and provides them as context.

Architecture components:

• Document ingestion pipeline with chunking and embedding
• Vector database for semantic search (Pinecone, Weaviate, Qdrant)
• Prompt construction with retrieved context
• Response validation and guardrails

Guardrail Implementation

Every LLM endpoint should be wrapped with input and output guardrails. Input guardrails detect prompt injection attempts. Output guardrails validate that responses conform to expected formats and don't contain prohibited content.