Understanding RAG architecture and its fundamentals | Computer Weekly
Summary
A larger number of chunks improves the accuracy of the results, but the multiplication of vectors increases the amount of resources and time required to process them. "Overlap ensures that there is always some margin between segments, which increases the chances of capturing important information even if it is split according to the initial chunking strategy," according to documentation from LLM platform Cohere. Note that it is sometimes useful to fine-tune the embeddings model when it does not contain sufficient knowledge of the language related to a specific domain, for example, oncology or systems engineering. Microsoft is also influential with DiskANN, an open source algorithm designed to obtain an ideal performance-cost ratio with large volumes of vectors, at the expense of accuracy. But there are very good reasons to host AI workloads on-premise • Advancing LLM precision & reliability - This is a guest post written by Ryan Mangan, datacentre and cloud evangelist and founder of Efficient Ether.