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Does this make it any way easier to replace lawyers with an LLM or expert system?

ivyirwin 3y ago
Not OP but working on a project in similar domain (ndaok.com). The technology is definitely making it easier to replace lawyers. The biggest barrier right now is lawyers themselves. In fact our project stopped trying to sell to lawyers because it's almost like they purposefully refuse to adapt new technology. Instead we've had success with customers trying to find a way not to use lawyers when they are not needed.
- Simon_ORourke 3y ago
  > trying to find a way not to use lawyers when they are not needed. Kudos to you guys, the elimination of the need for lawyers is up there with any societal issue you care to name. It may do more for social justice than funding anything else
lmeyerov 3y ago
This is the heart of most real generative AI systems for reasoning about text: index data using this basic technique (chunked document embeddings), and when talking to the AI, the AI looks up documents from these clusters and loads them in as context for making the answer. Many ways to improve over this, but it's the heart. In our case (louie.ai), users will have vector indexed their documents into a scalable database like OpenSearch/elasticsearch, or we help them do it, and they can talk to the data, visualize it, run analytics, etc. For example, "get everything on koala adoption from the last decade and draw as a clustered map" would generate a hybrid query to find "semantically similar" documents based on vectors and also symbolically on the time stamps, run it, and then decide to do the followup step of visualizing it using the same family of viz technique in the article. We haven't tried law yet, but already do this for areas like disaster, crime, & misinfo intelligence from social media & news. (Imagine: "Alert me when ..." or "summarize what..."). We find this approach fast and easy, but for very important questions, lower quality than we would like. Imagine a scenario like case law around koalas changing precedent over time. RAG using Langchain/LLMindex + OpenAI over a vector index doesn't solve that kind of thing out of the box. But they are solveable, and it's pretty fun to work through these kinda of issues :)

Not OP but working on a project in similar domain (ndaok.com). The technology is definitely making it easier to replace lawyers. The biggest barrier right now is lawyers themselves. In fact our project stopped trying to sell to lawyers because it's almost like they purposefully refuse to adapt new technology. Instead we've had success with customers trying to find a way not to use lawyers when they are not needed.

Simon_ORourke 3y ago
> trying to find a way not to use lawyers when they are not needed. Kudos to you guys, the elimination of the need for lawyers is up there with any societal issue you care to name. It may do more for social justice than funding anything else

lmeyerov 3y ago

This is the heart of most real generative AI systems for reasoning about text: index data using this basic technique (chunked document embeddings), and when talking to the AI, the AI looks up documents from these clusters and loads them in as context for making the answer. Many ways to improve over this, but it's the heart. In our case (louie.ai), users will have vector indexed their documents into a scalable database like OpenSearch/elasticsearch, or we help them do it, and they can talk to the data, visualize it, run analytics, etc. For example, "get everything on koala adoption from the last decade and draw as a clustered map" would generate a hybrid query to find "semantically similar" documents based on vectors and also symbolically on the time stamps, run it, and then decide to do the followup step of visualizing it using the same family of viz technique in the article. We haven't tried law yet, but already do this for areas like disaster, crime, & misinfo intelligence from social media & news. (Imagine: "Alert me when ..." or "summarize what..."). We find this approach fast and easy, but for very important questions, lower quality than we would like. Imagine a scenario like case law around koalas changing precedent over time. RAG using Langchain/LLMindex + OpenAI over a vector index doesn't solve that kind of thing out of the box. But they are solveable, and it's pretty fun to work through these kinda of issues :)