1. LLMs rely on training data that can quickly become outdated and may not include relevant domain-specific data.
2. Re-training or fine-tuning LLMs for fresh, domain-specific data can be expensive and complex.
3. RAG provides LLMs with access to domain-specific data without the need for re-training, reducing hallucinations and enabling applications to provide verifiable information.
LLMs rely on their training data, which can become outdated and lack relevant information for specific applications. Retraining or fine-tuning LLMs to incorporate fresh, domain-specific data can be costly and complex. However, RAG provides access to such data without the need for training or fine-tuning, guiding LLMs toward factual responses and reducing hallucinations, ultimately enabling applications to provide human-verifiable source material.
Generative AI has introduced new requirements for serving LLMs, processing unstructured data, and retrieving information. Customers seeking to build AI infrastructure for RAG applications may choose to access specialized platforms like Vertex AI or manage their own infrastructure on platforms like GKE while utilizing open-source frameworks and models. Making key decisions such as selecting frameworks for model serving and choosing machine shapes for inference is crucial when building an AI platform from scratch.
A quickstart solution and reference architecture for RAG applications on GKE, Cloud SQL, and frameworks like Ray, LangChain, and Hugging Face have been developed to help customers accelerate their journey to production. Benefits of deploying RAG on GKE and Cloud SQL include streamlined deployment, easy configuration of prerequisite resources, and access to AI frameworks like Ray and Hugging Face.
The end-to-end RAG application and reference architecture include setting up a Google Cloud project with necessary prerequisites, deploying AI frameworks to GKE, generating embeddings and populating Cloud SQL instances, and deploying a web-based RAG chatbot application. This solution aims to assist customers in quickly getting started with RAG applications and implementing best practices from the outset.