I deal mostly with tools such as Meta Llama for embeddings, and I was primarily using it for search, but now I use OpenSearch instead of Azure search, bringing some small language models and offline language models rather than cloud-based solutions. I am working with Meta Llama and have found the features especially valuable in the embeddings, as I use it along with OpenSearch for embeddings and building the RAG. We focus on RAG agents rather than AI agents, which require more offline RAG capabilities, and that's where I build them. I assess the benefits of Meta Llama's automation processes as substantial. First, I will make my customers happy without tokens because today, the cloud itself is unnecessarily expensive, and on top of that, tokens and security for public data add additional concerns. I ensure that we don't compromise on these three factors, especially from the compliance perspective. The metrics I use to measure the precision of insights from Meta Llama's capabilities are based on dropouts. A majority of my measurement involves assessing how much customers understand when we give some insights, and whether they directly click to create a quote, directly click to order a product, or directly move towards a solution rather than saying they would like to talk to a human. Especially in the CRM world, the more they interact with the CRM, the more I fail in AI. I have been a user, implementer, designer, and CTO and CIO for Meta Llama.
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I deal mostly with tools such as Meta Llama for embeddings, and I was primarily using it for search, but now I use OpenSearch instead of Azure search, bringing some small language models and offline language models rather than cloud-based solutions. I am working with Meta Llama and have found the features especially valuable in the embeddings, as I use it along with OpenSearch for embeddings and building the RAG. We focus on RAG agents rather than AI agents, which require more offline RAG capabilities, and that's where I build them. I assess the benefits of Meta Llama's automation processes as substantial. First, I will make my customers happy without tokens because today, the cloud itself is unnecessarily expensive, and on top of that, tokens and security for public data add additional concerns. I ensure that we don't compromise on these three factors, especially from the compliance perspective. The metrics I use to measure the precision of insights from Meta Llama's capabilities are based on dropouts. A majority of my measurement involves assessing how much customers understand when we give some insights, and whether they directly click to create a quote, directly click to order a product, or directly move towards a solution rather than saying they would like to talk to a human. Especially in the CRM world, the more they interact with the CRM, the more I fail in AI. I have been a user, implementer, designer, and CTO and CIO for Meta Llama.