As LLMs and embedding models gain prominence, developers are integrating them into applications via APIs, like those offered by OpenAI and Google Gemini. Given the substantial hardware and computing demands of LLMs, most are presented as network services. The blog emphasizes developing a Retrieval Augmented Generation (RAG) server using Go, showcasing both its efficiency and suitability for cloud-based applications. The RAG server allows users to add documents and query the model, thereby customizing its knowledge base for specific contexts.
Building LLM-powered applications is comparable to developing modern cloud-native applications, requiring notable support for REST and RPC protocols, where Go excels.
Retrieval Augmented Generation (RAG) is highlighted as a scalable method to customize LLMsâ knowledge bases for domain-specific interactions, improving user applications.
The blog focuses on constructing a RAG server in Go that enables users to add documents and ask questions based on the knowledge base.
The popularity of LLMs continues to rise, urging developers to integrate them into applications, typically via APIs due to their compute resource demands.
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