RAG guides for WordPress & WooCommerce

Fine-tuning a reranker with synthetic LLM generated data and LLM+human annotations

Great post from HumanSignal labelstud.io to fine-tune a Cohere reranker with synthetic LLM generated data and LLM+human annotations: Generate synthetic queries from documents with a LLM (OpenAI gpt4-o here) Extract results from your retrieval system for all synthetic queries Create a label project for reranking tasks with triplet-loss (positive, hard-negative) Upload query/results in the label studio Pre-label query/results with a LLM reranker’s back-end (OpenAI gpt4-o here) Let humans complete the pre-labeling Send labeled query/results to a LLM reranking fine-tuner (Cohere here) Test your new fine-tuned reranked retrieva Original post: https://labelstud.io/blog/improving-rag-document-search-quality-with-cohere-re-ranking/

What is RAG and how does it work

Nowadays, text generation is making a big wave thanks to LLMs (Large Language Models). Trained on large amounts of publicly (or sometimes privately) accessible data, these models can complete various language related tasks such as conversation (chatbot), question answering and even advising. They impact many sectors like writing, coding and marketing. But what about search? Well you’re at the right place because that is what RAG (Retrieval Augmented Generation) is about. What does RAG do ? RAG, as it’s name implies, combines both search and AI-based text generation. It has become a very trendy topic recently since it is capable of delivering the same capabilities as LLMs while remaining a more reliable source of information. This is because, when integrated into a RAG