DevOps KB Pages

The sequence is from "Text" to "Tokens," then "Vectors," and finally "Embeddings." In data processing and machine learning, the creation or extraction of a vector typically precedes the embedding process. Initially, data is converted into a vector form, which is a numerical representation, and then, embeddings are generated from these vectors. Embeddings are lower-dimensional representations that capture the relationships and features of the data, making it easier to use in various machine learning models.  This generalized concept reflects the idea that vectors serve as the foundation upon which embeddings are built. When a model is trained, the initial vectorized values (those random vectors assigned at the beginning) don’t exist separately after the embeddings are created. Here’s how it works: Training Process: Initial Vectors: These are the starting points, just random numbers. They exist at the beginning of the training process but are not ...

What is RAG (Retrieval-Augmented Generation)? Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response. Large Language Models (LLMs) are trained on vast volumes of data and use billions of parameters to generate original output for tasks like answering questions, translating languages, and completing sentences. RAG extends the already powerful capabilities of LLMs to specific domains or an organization's internal knowledge base, all without the need to retrain the model. It is a cost-effective approach to improving LLM output so it remains relevant, accurate, and useful in various contexts. Related Blogs: KB-OpenAI References: What Is RAG? Best Practices in Retrieval Augmented Generation