DataHour: Optimizing LLMs with Retrieval Augmented Generation and Haystack 2.0

DataHour: Optimizing LLMs with Retrieval Augmented Generation and Haystack 2.0

15 Dec 202313:12pm - 15 Dec 202314:12pm

DataHour: Optimizing LLMs with Retrieval Augmented Generation and Haystack 2.0

About the Event

How great would it be if LLMs could do some research before answering your questions, instead of hallucinating a random answer, when it doesn’t know what to say?

Retrieval Augmented Generation, or RAG, is one of the most solid and battle-tested techniques to strongly reduce hallucination and give LLMs access to information that it didn’t have at training time. With RAG there’s no need to fine-tune a GPT model or scale up your backend with GPUs. RAG is a lightweight technique that can bring all these capabilities to your models for cheap.

In this live coding session, we are going to implement a simple RAG pipeline with the open-source framework Haystack. Starting is as easy as connecting together three components, and then we will expand its capabilities to other powerful use cases, such as web LFQA, by adding more components.

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Who is this DataHour for?

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About the Speaker

Sara Zanzottera

Sara Zanzottera

NLP Engineer at Deepset

Sara Zanzottera is an NLP Engineer at deepset as a core maintainer of Haystack, one of the most mature open-source LLM frameworks. Before joining Deepset she worked for several years at CERN as a Python software engineer for the accelerator’s control systems.

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