Low-Hanging Fruit for RAG Search - jxnl.co
jxnl.co
Low-Hanging Fruit for RAG Search - jxnl.co
Most large language models (LLMs) rely on reinforcement learning (RL) to refine how “helpful and harmless” they sound. Notoriously, OpenAI has used cheap labor in Kenya to label and filter toxic outputs, fine-tuning its models to produce more acceptable language.
DeepSeek R1 took a different path: Instead of focusing on sounding right, it zeroes in
... See moreEvan Armstrong • What Actually Matters (And What Doesn’t) for DeepSeek
Ragas is a framework that helps you evaluate your Retrieval Augmented Generation (RAG) pipelines. RAG denotes a class of LLM applications that use external data to augment the LLM’s context. There are existing tools and frameworks that help you build these pipelines but evaluating it and quantifying your pipeline performance can be hard. This is wh... See more
explodinggradients • GitHub - explodinggradients/ragas: Evaluation framework for your Retrieval Augmented Generation (RAG) pipelines
You use state-of-the-art Retrieval Augmented Generation technology. How does that work?
Retrieval augmented generation (RAG) is an up and coming technique that addresses the pitfalls of large language models, particularly in the realm of AI hallucinations. It uses a mix of both good information retrieval and generative AI to generate answers which ... See more
Retrieval augmented generation (RAG) is an up and coming technique that addresses the pitfalls of large language models, particularly in the realm of AI hallucinations. It uses a mix of both good information retrieval and generative AI to generate answers which ... See more
Interview: Supercharge your thinking with the co-founders of Beloga
Retrieval-augmented generation (RAG) is a technique that enhances text generation by retrieving and incorporating external knowledge.