https://labintrace.com/tour I am building an AI governance architecture for regulated environments using publicly available cited CAP/CLSI/Hl7 standards. This framework also incorporate a Qdrant RAG pipeline for locality based lab SOPs, evidence based reg reviews which are encoded into a knowledge based structure.
They still have their place, for example, using RLM + progressive disclosure works, but it's super slow. So if you want something snappy or cheaper, you still need to implement a normal index on top of it.
But for knowledge work, using RLMs is a gold mine as you can query any type of data with minimal effort
Amazing article. I’ll definitely implement this into a real scenario in the next weeks. Thank you :)
My pleasure! Ping me with what you built. Would love to take a look
I wonder, Is RLM a harnesses agent ?
and will the harness agent replace RAG pipeline ? (chunk,embed, retrieve)
Yes, you can think of RLM as an orchestration technique used in harnesses.
Depending on your app, it can replace RAG or complement it.
What are you thinking of?
This is cool, Paul. I think works well for regulated type industries (healthcare, pharma) where many documents are just not well indexed.
yes! if latency is not a problem, this solution works amazingly for me. What are you working on?
https://labintrace.com/tour I am building an AI governance architecture for regulated environments using publicly available cited CAP/CLSI/Hl7 standards. This framework also incorporate a Qdrant RAG pipeline for locality based lab SOPs, evidence based reg reviews which are encoded into a knowledge based structure.
I think that the expensive tasks (such as ingest data, embedding a large of data, gen metadata) will not good to use harnesses agent.
They still have their place, for example, using RLM + progressive disclosure works, but it's super slow. So if you want something snappy or cheaper, you still need to implement a normal index on top of it.
But for knowledge work, using RLMs is a gold mine as you can query any type of data with minimal effort