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Advanced RAG Techniques: an Illustrated Overview | by IVAN ILIN | Dec, 2023 | Towards AI

pub.towardsai.net · 4,866 words · saved by 1 readers

Since the goal of the post is to make an overview & explanation of avaliable RAG algorithms and techniques, I won’t dive into implementations details in code, just referencing them and leaving it to the vast documentation & tutorials available. If you are familiar with the RAG concept, please skip to the Advanced RAG part. Retrieval Augmented Generation, aka RAG, provides LLMs with the information retrieved from some data source to ground its generated answer on. Basically RAG is Search + LLM prompting, where your ask the model to answer the query provided the information found with the search algorithm as a context. Both the query and the retrieved context are injected into the prompt that is sent to the LLM. RAG is the most popular architecture of the LLM based systems in 2023. There are many products build almost solely on RAG — from Question Answering services combining web search engines with LLMs to hundreds of chat-with-your-data apps. Even the vector search area got pumped by t

Groningen, Martinitoren, where the article was composed in the peace of the Noorderplatsoen ML LLM NLP Retrieval Augmented AI Advanced RAG Techniques: an Illustrated Overview IVAN ILIN 19 min read · Dec 17, 2023 -- 40 Listen Share A comprehensive study of the advanced retrieval augmented generation techniques and algorithms, systemising various approaches. The article comes with a collection of links in my knowledge base referencing various implementations and studies mentioned. Since the goal of the post is to make an overview & explanation of avaliable RAG algorithms and techniques, I won’t

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