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anyblockers.com
| | amirmalik.net
4.6 parsecs away

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| | An introduction to Retrieval-Augmented Generation (RAG) and how embeddings, chunking, and vector search work together in the context of LLM search.
| | newvick.com
2.9 parsecs away

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| | RAG is not all you need. This post will cover some of the common problems that are encountered in a simple RAG system, and potential solutions for them.
| | ninkovic.dev
4.3 parsecs away

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| | First part in the blog series of how to build a RAG (Retrieval Augmented Generation) system from scratch. Aimed at beginners, the blog will introduce you to foundational concepts and pieces which are needed to build such a system. The blog comes with code and step by step implementation.
| | amatria.in
15.1 parsecs away

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| [AI summary] The provided text is an extensive overview of various large language models (LLMs) and their architectures, training tasks, and applications. It includes detailed descriptions of models like GPT, T5, BERT, and others, along with their pre-training objectives, parameter counts, and specific use cases. The text also references key research papers, surveys, and resources for further reading on LLMs and related topics.