GenAI App Architecture Explained (Part 2: Completing the Big Picture)
In the first article , we outlined the high-level architecture of a modern GenAI application: the orchestrator, the embeddings model, the vector database, and the external actions or tools. Today, we complete that picture by adding the supporting components that make the system resilient, observable, and reliable . The often-overlooked building blocks In the diagram above, these gray blocks are the hidden backbone of any production-grade GenAI app. Let's focus on the 3 remaining ones. LLM Cache This is where previously generated model responses are stored so they can be reused later. The goal: reduce latency and avoid unnecessary calls to expensive models. Typical tools: Redis, SQLite, GPTCache. Caching is crucial for both cost control and responsiveness . When a user asks the same or a very similar question, the system can serve a cached response instead of re-querying the model. But caching in the LLM context is not as straightforward as caching a static API response. Queries ...