Assistant – 6/18-2/25, per Bank of America Erica acts as both a personal concierge and mission control for our clients. Our data science team has made more than 50,000 updates to Erica’s performance relative to prior analytical techniques with the remainder relative to a random baseline or holdout control.’ We indicate 2020 as the start year for JP Morgan’s AI Modernization (2020 Letter to Shareholders: Lead qualification • Order tracking • Control computer screen directly to perform tasks like pulling data from websites, making online purchases, etc. • Control computer screen directly to perform tasks

.args) Use hybrid script as an alternative text format Directly write pass, manipulate IR structures Accelerate innovation, 
 e.g. use (GA/RL/BayesOpt/your favorite ML method) for AutoSchedule 


a code change, 
 or generating a report. Applications that integrate LLMs but don’t use them to control workflow execution—think simple chatbots, single-turn LLMs, or sentiment classifiers—are not agents proactively correct its actions if needed. In case 
 of failure, it can halt execution and transfer control back to the user. 02 It has access to various tools to interact with external systems—both to gather orchestrate a network of specialized agents seamlessly through tool calls. Instead of losing context or control, the manager intelligently delegates tasks to the right agent at the right time, effortlessly synthesizing

Design with simplicity 55 Be specific about the output 56 Use Instructions over Constraints 56 Control the max token length 58 Use variables in prompts 58 Experiment with input formats and writing styles need to figure out the model configuration. Most LLMs come with various configuration options that control the LLM’s output. Effective prompt engineering requires setting these configurations optimally for higher, all tokens become equally likely to be the next predicted token. The Gemini temperature control can be understood in a similar way to the softmax function used in machine learning. A low temperature

dynamism ● Control flow (if, loop, etc) ● Dynamic shapes ○ Dynamic inputs: batch size, image size, sequence length, etc. ○ Output shape of some ops are data dependent: arange, nms, etc. ○ Control flow:

at a glance For our enterprise customers, nothing is more important than security, privacy and control. 
 Here’s how we ensure it: Your data stays yours We don’t use your content to train our models;

cost. As we employ expert parallelism during training, we also devise supplementary mechanisms to control communication overheads and ensure load balance. By combining these two techniques, DeepSeek-V2 features