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performance since launch – adjusting, expanding and fine-tuning natural language understanding capabilities, ensuring answers and insights remain timely and relevant. 2 billion client interactions is facilitate scribe-like capabilities in real time, [have] great potential to reduce documentation burden, enhance physician-patient encounters, and augment clinicians’ capabilities. The technology leverages (TPMG) enabled ambient AI technology for 10,000 physicians and staff to augment their clinical capabilities across diverse settings and specialties. - New England Journal of Medicine Catalyst Research

models here. 8 A practical guide to building agents Defining tools Tools extend your agent’s capabilities by using APIs from underlying applications or systems. For legacy systems without APIs, agents keeping complexity manageable and simplifying evaluation and maintenance. Each new tool expands its capabilities without prematurely forcing you to orchestrate multiple agents. Tools Guardrails Hooks Instructions to consider creating multiple agents Our general recommendation is to maximize a single agent’s capabilities first. More agents can provide intuitive separation of concepts, but can introduce additional

on a few factors, including the complexity of the task, the quality of the examples, and the capabilities of the generative AI (gen AI) model you are using. As a general rule of thumb, you should use serves a slightly different primary purpose: • System prompt: Defines the model’s fundamental capabilities and overarching purpose. • Contextual prompt: Provides immediate, task-specific information to of Thought (CoT) Chain of Thought (CoT) 9 prompting is a technique for improving the reasoning capabilities of LLMs by generating intermediate reasoning steps. This helps the LLM generate more accurate

mlplatform.org/ ● Git and review servers ● Forums and issue tracker ● Public mailing lists and IRC channel ● Internal Jira project restricted to Linaro members ● Three sub-projects: ○ Arm Compute Library

around three teams. Our Research Team advances the foundations of AI, developing new models and capabilities. Our Applied Team turns those models into products, like ChatGPT Enterprise and our API. And this specific job was recommended to them. Indeed uses the data analysis and natural language capabilities of GPT-4o mini to shape these ‘why’ statements in their emails and messages to jobseekers. Using

an LLM tends to improve as the number of parameters increases, allowing it to exhibit emergent capabilities across various tasks (Wei et al., 2022). However, the improvement comes at the cost of larger English tokens. Therefore, we acknowledge that DeepSeek-V2 still has a slight gap in basic English capabilities with LLaMA3 70B. However, even with much fewer training tokens and activated parameters, DeepSeek-V2 Our observation underscores the critical need for sufficient data to equip an LLM with desired capabilities. Moreover, the quality of SFT data is also crucial, especially for tasks involving writing or