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franciscojavierarceo merged 11 commits into from
Apr 3, 2025
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feat: Adding blog on RAG with Milvus #5161

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feat: Adding blog on RAG with Milvus
franciscojavierarceo Mar 17, 2025
13e4330
minor changes
franciscojavierarceo Mar 17, 2025
65211f5
Adding diagram
franciscojavierarceo Mar 26, 2025
a9b2a1a
Rename image.png to milvus-rag.png
franciscojavierarceo Mar 26, 2025
027cb4b
Update retrieval-augmentation-with-feast.md
franciscojavierarceo Mar 26, 2025
6d7e1ac
Merge branch 'feast-dev:master' into milvus-blog-post
franciscojavierarceo Apr 2, 2025
4685750
incorporating Willem's feedback
franciscojavierarceo Apr 2, 2025
ee53d42
adjust blog and image
franciscojavierarceo Apr 2, 2025
7e1e3d2
updated copy
franciscojavierarceo Apr 2, 2025
ce1b114
adding github link
franciscojavierarceo Apr 2, 2025
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finished blog post, good enough
franciscojavierarceo Apr 3, 2025
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minor changes 
Signed-off-by: Francisco Javier Arceo <arceofrancisco@gmail.com>
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franciscojavierarceo committed Mar 17, 2025
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6 changes: 3 additions & 3 deletions infra/website/docs/blog/retrieval-augmentation-with-feast.md
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Expand Up @@ -12,14 +12,14 @@ authors: ["Francisco Javier Arceo"]
# Is a Feature Store a good fit for GenAI use cases?
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Yes, a Feature Store is a great fit for GenAI use cases!

That's because Feature Stores were developed over the last 10 years to explicitly handle the problems facing AI
practitioners. The Feast maintainers will continue to invest in making the GenAI development experience a first-class
That's because Feature Stores were developed over the last 10 years to explicitly handle the problems AI
practitioners faced when working with data. The Feast maintainers will continue to invest in making the GenAI development experience a first-class
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citizen so that you can rely on Feast to customize your AI applications. If you have thoughts or ideas
feel free to reach out!
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# What is Retrieval Augmented Generation (RAG)?
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@woop woop Mar 17, 2025

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This section is fine, but can probably be simplified. We can assume readers know what RAG is and dont need to be educated on it.

[RAG](https://en.wikipedia.org/wiki/Retrieval-augmented_generation) is a technique that combines generative models
(e.g., LLMs) with retrieval systems to produce to generate contextually relevant output for a particular goal (e.g.,
(e.g., LLMs) with retrieval systems to generate contextually relevant output for a particular goal (e.g.,
question and answering).

The typical RAG process involves:
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