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Beyond Proof of Concept: Building RAG Systems That Scale
- The "LLM Twin: Building Your Production-Ready AI Replica" free course teaches how to design, train, and deploy an LLM twin AI character that writes like you by incorporating your style, personality, and voice into an LLM. This article discusses lesson 9 out of the 12 lessons of this course, which focuses on building an LLM system that scales beyond the proof of concept stage. The end goal is to build and deploy the LLM Twin, and this lesson discusses how to hook together the key components of the AI inference pipeline in a scalable and modular system architecture.
- The article discusses two options to design the inference pipeline: monolithic LLM & business service and different LLM & business microservices. Decoupling the components empowers scaling individually as required, providing a cost-effective solution to meet the system's needs.
- The article then describes how the microservice pattern is applied to the concrete LLM twin inference pipeline. The components include LLM microservice deployed on AWS SageMaker as an inference endpoint, a prompt monitoring microservice based on Opik (an open-source LLM evaluation and monitoring tool powered by Comet ML), and a business microservice implemented as a Python module that glues all the domain steps together and delegates the computation to other services.
- The article further illustrates the differences between training and inference pipelines, and the core differences you have to understand, which are accessed from an offline data store and an online database optimized for low latency, respectively.
- The article explains how to deploy the LLM microservice and how to test the inference pipeline by running a Gradio chat GUI. The article concludes by summarizing the major points discussed in the lesson on scaling the LLM architecture.
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