C2S Technologies, Inc. reposted this
Introducing new synthetic data capabilities in Mosaic AI Agent Evaluation. Now, developers can create high-quality evaluation data sets based on their proprietary data in just minutes – without being bottlenecked by SMEs. The result is actionable insights tailored to an organization’s unique use cases that enhance agent quality. See the demo and how to quickly get started: https://dbricks.co/4ioBi8X
As of today, is there any out-of-the-box LLM hosted in Databricks that can read requirements documents & generate test cases as well as test case SQL to verify the test case results? Appreciate the guidance.
Is this not just a feed back loop? There is really no alternative to garbage in and garbage out
Creating high-quality evaluation data sets in minutes—without the bottleneck of SMEs—means faster, more actionable insights tailored to specific use cases. This is a huge leap forward in enhancing agent quality! #AI #DataInnovation #SyntheticData #MosaicAI #Databricks
Lol, I can’t believe Databricks is marketing these minor features. #Choona
This is great
This could be a huge leap forward. We often see clients facing a bottleneck in agent development caused by labour-intensive validation/evaluation processes. If automating the creation of high-quality evaluation datasets offers a means to accelerate this it WOULD mean faster time to production and COULD mean a higher agent quality to time spend ratio - basically a much more efficient development cycle. In this case, this approach would be the new standard. Personally I’d exercise some caution before fully embracing this approach though. Relying heavily on generated evaluation datasets to test AI agents could activate a lot of risks - off the top of my head it might mean a less exhaustive/realistic test, or lead technicians to overlook standard data quality procedure - in both cases forcing improper quantification of agent performance. Despite accelerating certain steps then, this might not translate into agent quality improvements - though obviously in some cases this trade off will be worth it anyway. Before using this I'd like to see how the data generated fares with regard to data quality risk via a small-scale pilot, and thereby work out if the value is faster dev, better agents, or (the dream scenario) BOTH.