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An explaination of retrieval augmented generation (part IV)

In the domain of RAG, the standard practice often involves a singular (once) retrieval step followed by generation, which can lead to efficiencies and sometimes is typically insufficient for complex problems demanding multi-step reasoning, as it provides a limited scope of information.

An explaination of retrieval augmented generation (part III)

After retrieval, it is not a good practice to directly input all the retrieved information to the LLM for answering questions.

An explaination of retrieval augmented generation (part II)

Coming up next the last part of the RAG series, in this part II, we focus on Advanced RAG, which introduces specific improvements to overcome the limitations of Naive RAG.

Exploring the Different Variants of Transformer Models (Part I)

Even though there’s been an explosion of different transformer models lately, they can still be grouped into three main categories: Encoders, Decoders, and Encoder-Decoders.

An explaination of retrieval augmented generation (part I)

Recent years have witnessed a surge in the progress of Generative AI (GenAI), particularly Large Language Models (LLMs), which have revolutionized how humans can search for and access vital information.