Row 5294

Row ID: 5294 | Dataset Entry | Axioma AXP Content Repository

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Hi everyone,

In my experience, my company does a lot of work on LLMs and I can say with absolute certainty that those projects are permutations and combinations of making an intelligent chatbot which can chat with your proprietary documents, summarize information, build dashboards and so on. I've prototyped these RAG systems (nothing in production, thankfully) and am not enjoying building them. I also don't like the LLM framework wars (Langchain vs Llamaindex vs this and that - although, Langchain sucks in my opinion).

What I am interested in putting my data scientist / (fake) statistician hat back on and approach LLMs (and related topics) from a research perspective. What are the problems to solve in this field? What are the pressing research questions? What are the topics that I can explore in my personal (or company) time beyond RAG systems?

Finally, can anyone explain what the heck is agentic AI? Is it just a fancy buzzword for this sentence from Russell and Norvig's magnum opus AI book- " A rational agent is one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome".

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FieldValue
text Hi everyone, In my experience, my company does a lot of work on LLMs and I can say with absolute certainty that those projects are permutations and combinations of making an intelligent chatbot which can chat with your proprietary documents, summarize information, build dashboards and so on. I've prototyped these RAG systems (nothing in production, thankfully) and am not enjoying building them. I also don't like the LLM framework wars (Langchain vs Llamaindex vs this and that - although, Langch…
label r/datascience
dataType post
communityName r/datascience
datetime 2024-04-28
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url_encoded Z0FBQUFBQm5Lak9GQk1MMmM0VXlMQTRtd181eVlHS1ZxUDJLTTBmZGk1SXVGc3B3UTZQNDJvTWczYXVDZVhPYk9lMDNGQmFwUDloRlFmS2lSUThJOFVDQWdEd3lvTldySnpNNGpabFh0a3puUFQ0YUNicjZNME9CalpXczVfWV9kUWx2LVphcXZ4ZlFDaUhrd2ZycnA4UWtGbFcydmhnRE41bFpFYlNMNmJIVUFxOXJtaUxTZ3YtMXJRZ1lhQWp1Wi1SUTF5MmpvZWNCaHoydXNkYWZnM29ud01HeWx0TnJDZz09

Raw Record

{
  "text": "Hi everyone,\n\nIn my experience, my company does a lot of work on LLMs and I can say with absolute certainty that those projects are permutations and combinations of making an intelligent chatbot which can chat with your proprietary documents, summarize information, build dashboards and so on. I've prototyped these RAG systems (nothing in production, thankfully) and am not enjoying building them. I also don't like the LLM framework wars (Langchain vs Llamaindex vs this and that - although, Langchain sucks in my opinion).   \n\n\nWhat I am interested in putting my data scientist / (fake) statistician hat back on and approach LLMs (and related topics) from a research perspective. What are the problems to solve in this field? What are the pressing research questions? What are the topics that I can explore in my personal (or company) time beyond RAG systems?\n\nFinally, can anyone explain what the heck is agentic AI? Is it just a fancy buzzword for this sentence from Russell and Norvig's magnum opus AI book- \" A rational agent is one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome\".   \n\n\n  \n\n\n​",
  "label": "r/datascience",
  "dataType": "post",
  "communityName": "r/datascience",
  "datetime": "2024-04-28",
  "username_encoded": "Z0FBQUFBQm5LakwyZkhBaElha3dJYjYtejR5OVBuSWVyV0NjMzhXckJEdE1rRDdndnpabDByNk9Pa2dCNk1wQ25BNkwyTnlBWXdIdkY0bjlWbHR3OVY3c1dyQkh1TV9lb3c9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9GQk1MMmM0VXlMQTRtd181eVlHS1ZxUDJLTTBmZGk1SXVGc3B3UTZQNDJvTWczYXVDZVhPYk9lMDNGQmFwUDloRlFmS2lSUThJOFVDQWdEd3lvTldySnpNNGpabFh0a3puUFQ0YUNicjZNME9CalpXczVfWV9kUWx2LVphcXZ4ZlFDaUhrd2ZycnA4UWtGbFcydmhnRE41bFpFYlNMNmJIVUFxOXJtaUxTZ3YtMXJRZ1lhQWp1Wi1SUTF5MmpvZWNCaHoydXNkYWZnM29ud01HeWx0TnJDZz09"
}
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