Row 5844

Row ID: 5844 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 5844 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

At it's essence I guess RAG is about

1. retrieving relevant documents based on the prompt 2. putting the documents into the context window

Number 2 is very straight forward, while number 1 is where I guess more of the important stuff happens. IIRC, most often we do a similarity search here between the prompt embedding and the document embeddings, and retrieve the k-most similar documents.

Ok, at this point we have k documents and put them into context. Now it's time for the LLM to give me an answer based on my prompt and the k documents, which a good LLM should be able to do given that the correct documents were retrieved.

I tried doing some hobby projects with LlamaIndex but didn't get it to work so nicely. For example, I tried with NFL statistics as my data (one row per player, one column per feature) and hoped that GPT-4 together with these documents would be able to answer atleast 95% of my question correctly, but it was more like 70% which was surprisingly bad since I feel like this was a fairly basic project. Questions were of the kind "how many touchdowns did player x do in season y". Answers varied from being correct, to saying the information wasn't available, to hallucinating an incorrect answer.

Hopefully I'm just doing something in suboptimal way, but it got me thinking of how widely used RAG is in production around the world. What are some applications on the market that successfully utilizes RAG? I assume something like [perplexity.ai](https://perplexity.ai) is using it, and of course all other chatbots that uses browsing in some way. An obvious application mentioned is often embedding your company documents, and then having an internal chatbot that uses RAG. Is that deployed anywhere? Not at my company, but I could see it being useful.

Basically, is RAG mostly something that sounds good in theory and is currently hyped or is it actually something that is used in production around the world?

FieldValue
text At it's essence I guess RAG is about 1. retrieving relevant documents based on the prompt 2. putting the documents into the context window Number 2 is very straight forward, while number 1 is where I guess more of the important stuff happens. IIRC, most often we do a similarity search here between the prompt embedding and the document embeddings, and retrieve the k-most similar documents. Ok, at this point we have k documents and put them into context. Now it's time for the LLM to give me an …
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-04
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Raw Record

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  "text": "At it's essence I guess RAG is about\n\n1. retrieving relevant documents based on the prompt\n2. putting the documents into the context window\n\nNumber 2 is very straight forward, while number 1 is where I guess more of the important stuff happens. IIRC, most often we do a similarity search here between the prompt embedding and the document embeddings, and retrieve the k-most similar documents.\n\nOk, at this point we have k documents and put them into context. Now it's time for the LLM to give me an answer based on my prompt and the k documents, which a good LLM should be able to do given that the correct documents were retrieved.\n\nI tried doing some hobby projects with LlamaIndex but didn't get it to work so nicely. For example, I tried with NFL statistics as my data (one row per player, one column per feature) and hoped that GPT-4 together with these documents would be able to answer atleast 95% of my question correctly, but it was more like 70% which was surprisingly bad since I feel like this was a fairly basic project. Questions were of the kind \"how many touchdowns did player x do in season y\". Answers varied from being correct, to saying the information wasn't available, to hallucinating an incorrect answer.\n\nHopefully I'm just doing something in suboptimal way, but it got me thinking of how widely used RAG is in production around the world. What are some applications on the market that successfully utilizes RAG? I assume something like [perplexity.ai](https://perplexity.ai) is using it, and of course all other chatbots that uses browsing in some way. An obvious application mentioned is often embedding your company documents, and then having an internal chatbot that uses RAG. Is that deployed anywhere? Not at my company, but I could see it being useful.\n\nBasically, is RAG mostly something that sounds good in theory and is currently hyped or is it actually something that is used in production around the world?",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-04",
  "username_encoded": "Z0FBQUFBQm5LakwyTVRfNjNoMG1xQ2IybmtEMTA1TTVtTm5PSVRyQjN5azAyM2tHYjJZOEdNdUthMDNOLXZ0S0ZkSUl1YXRTRS00bWFNQm85d1lxc2lFQVVGSm9XSGNEcnc9PQ==",
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}
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