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I think search is the wrong lens to look at it. Yes, finding relevant information quickly is important, but the key to enterprise search tools would be to get a

I think search is the wrong lens to look at it. Yes, finding relevant information quickly is important, but the key to enterprise search tools would be to get a holistic view around any topic. A typical enterprise has a lot of silos (12 out of top 15 enterprise apps on G2 are addressing this problem) and the flow of information doesn't exist. Any enterprise search tool helps in aggregation and triangulation of conversations/knowledge from various sources, helping any user get the sense of 1/ what is the current state of this topic? 2/ how did they get to this state? (you would have guessed it, you need a knowledge graph for the product to work well) (Disclaimer: we started with enterprise search too, and now we think a custom model is a better way to get to those goals) Also, the output needs to be integrated into their own workflows. Eg: Seeing a conversation on slack and using a bot fetch all the supplemental information to understand the context from say mail, docs etc. is mighty useful. Search using RAG is the easy part, the hard part is contextualizing it in a way it is immediately useful. That depends on understanding the company/domain lingo, understanding users, etc. Private aspect is a bad UX but not a bottleneck. Remember, this is targeted towards power users looking to use it on a daily basis. Connecting the bot once to get started is fine as long as it adds value. If anyone offers data governance along with it (updating access on a daily basis and answers only from access), it could be a huge hit.

yuhongsun 3y ago
Exactly, there is a huge amount of value in being able to quickly get a holistic view of topics. Most topics don't exist in an isolated tool - most often there are the official discussions/designs which exist in place, there are customer interactions with the topic which uses a separate channel, and then there are one off small conversations about the topic in chats like Slack. So isolated, tool specific searches are great for finding specific documents, but less useful for getting actionable insights. Regarding contextualizing: we're currently working on organizational understanding and we're very excited about this one! We're embedding users based on the documents they authored or interacted with, the questions they have asked, description of projects they worked on, and the org chart. The thinking is that, there will always be questions that can't fully be answered via documentation alone. But in those cases we'll be able to recommend someone who might know. It also has the benefit of contextualizing the user asking so that we can surface more relevant results for them.
baetylus 3y ago
>> the hard part is contextualizing it in a way it is immediately useful. That depends on understanding the company/domain lingo, understanding users, etc. How do you control this deterministically? It sounds like the "hard part" is variation in prompting & selectively choosing the right data to include, both of which I could see being good enough right now but hard to deliver definitively.
- yuhongsun 3y ago
  Being able to filter down the data deterministically is a big value add, especially as the number of documents scale into the range of multiple millions. We have filters by document-set, tags, time range, source type (ie. only include Slack + Google Drive, or Confluence + Jira + Gong, etc.) The challenge is with the non-deterministic portions of the flow as you pointed out. Ensuring retrieval quality in out-of-domain datasets, guardrailing the LLM generation, working with conflicting or deprecated information are some of the interesting areas we're addressing. Happy to dive deeper on any aspect you're curious about, and I'm sure we can learn from the discussion as well.

yuhongsun 3y ago

Exactly, there is a huge amount of value in being able to quickly get a holistic view of topics. Most topics don't exist in an isolated tool - most often there are the official discussions/designs which exist in place, there are customer interactions with the topic which uses a separate channel, and then there are one off small conversations about the topic in chats like Slack. So isolated, tool specific searches are great for finding specific documents, but less useful for getting actionable insights. Regarding contextualizing: we're currently working on organizational understanding and we're very excited about this one! We're embedding users based on the documents they authored or interacted with, the questions they have asked, description of projects they worked on, and the org chart. The thinking is that, there will always be questions that can't fully be answered via documentation alone. But in those cases we'll be able to recommend someone who might know. It also has the benefit of contextualizing the user asking so that we can surface more relevant results for them.

baetylus 3y ago

>> the hard part is contextualizing it in a way it is immediately useful. That depends on understanding the company/domain lingo, understanding users, etc. How do you control this deterministically? It sounds like the "hard part" is variation in prompting & selectively choosing the right data to include, both of which I could see being good enough right now but hard to deliver definitively.

yuhongsun 3y ago
Being able to filter down the data deterministically is a big value add, especially as the number of documents scale into the range of multiple millions. We have filters by document-set, tags, time range, source type (ie. only include Slack + Google Drive, or Confluence + Jira + Gong, etc.) The challenge is with the non-deterministic portions of the flow as you pointed out. Ensuring retrieval quality in out-of-domain datasets, guardrailing the LLM generation, working with conflicting or deprecated information are some of the interesting areas we're addressing. Happy to dive deeper on any aspect you're curious about, and I'm sure we can learn from the discussion as well.