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Add RAG blogpost announcement (#777)
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date: | ||
created: 2024-01-15 | ||
authors: | ||
- mrchtr | ||
--- | ||
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# Let's tune RAG pipelines with Fondant | ||
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Retrieval Augmented Generation (RAG) has quickly become the go-to architecture for providing large | ||
language models (LLM) with specific knowledge. Optimizing a custom setup requires days to find the | ||
right set of parameters and system configuration. | ||
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We have created an example use case to show how you can enhance your RAG setup by using Fondant. | ||
Checkout out the resources: | ||
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- [GitHub repository](https://github.com/ml6team/fondant-usecase-RAG) | ||
- [Blog post](https://medium.com/fondant-blog/lets-tune-rag-pipelines-with-fondant-902f7215e540) | ||
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Off-the-shelf solutions might be easy to set up for a quick proof of concept, but their performance | ||
is usually insufficient for production usage since they are not adapted to the complexities of | ||
specific situations. | ||
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By employing several different methods in an iterative manner, we can more than double the accuracy | ||
of an RAG system and maximize its performance. We have built two Fondant pipelines that can assist | ||
you in finding the optimal combinations of parameters for your unique setup. | ||
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![RAG finetuning pipelines](../../art/posts/rag_finetuning.png) | ||
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In the [example repository](https://github.com/ml6team/fondant-usecase-RAG), you can find notebooks | ||
that will help you customize your setup. More information can be found in | ||
the [blog post](https://medium.com/fondant-blog/lets-tune-rag-pipelines-with-fondant-902f7215e540). | ||
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