It would be better to have a dashboard for users to showcase how reranking helps improve quality. When end users choose the service, they want to see the actual output. The evaluation part is challenging for recent large language model applications but remains very important. If Cohere could provide a dashboard where we can employ an LLM as a judge to check quality before and after reranking, that would be helpful. We could either have another large language model evaluate this part or allow UAT users to manually check with humans in the middle. As an enterprise provider, we want such features because when chatting with clients, we can demonstrate that employing Cohere's reranking model significantly improves results compared to not using it. Documentation is not a major blocking issue for us as we are sophisticated software engineers. Integration and the API provided for reranking models are not complicated, so we can easily handle that. The documentation is good. The major point is to prove the value through evaluation. We need a sophisticated solution to showcase visibly to our clients and engineering team to convince them that using this model creates improvements.
When performing similarity matching between text descriptions and the catalog descriptions created using Cohere, the matching could be improved. Because it does not have extensive understanding of Oracle functionalities in ERP, it sometimes gives wrong results or the confidence score is lower than desired. Improving that understanding would provide better matches. When working with Cohere and providing large data sets, there was some hallucination, though it mostly works fine without many issues.
Cohere has text generation. I think it is mainly focused on AI search. If there was a way to combine the searches with images, I think it would be nice to include that.
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It would be better to have a dashboard for users to showcase how reranking helps improve quality. When end users choose the service, they want to see the actual output. The evaluation part is challenging for recent large language model applications but remains very important. If Cohere could provide a dashboard where we can employ an LLM as a judge to check quality before and after reranking, that would be helpful. We could either have another large language model evaluate this part or allow UAT users to manually check with humans in the middle. As an enterprise provider, we want such features because when chatting with clients, we can demonstrate that employing Cohere's reranking model significantly improves results compared to not using it. Documentation is not a major blocking issue for us as we are sophisticated software engineers. Integration and the API provided for reranking models are not complicated, so we can easily handle that. The documentation is good. The major point is to prove the value through evaluation. We need a sophisticated solution to showcase visibly to our clients and engineering team to convince them that using this model creates improvements.
When performing similarity matching between text descriptions and the catalog descriptions created using Cohere, the matching could be improved. Because it does not have extensive understanding of Oracle functionalities in ERP, it sometimes gives wrong results or the confidence score is lower than desired. Improving that understanding would provide better matches. When working with Cohere and providing large data sets, there was some hallucination, though it mostly works fine without many issues.
Cohere has text generation. I think it is mainly focused on AI search. If there was a way to combine the searches with images, I think it would be nice to include that.