They decided to get AI into the hands of employees—working closely with Legal, Compliance, and IT Security teams to ensure responsible use. They rolled out ChatGPT Enterprise globally, then let people discover now build consistently 
 high-quality apps, faster, without having to get into the source code. Security, guardrails, and routing logic are all built in. 18 AI in the EnterpriseAs a result, AI app development the EnterpriseThe trusted AI enterprise platform Security and privacy at a glance For our enterprise customers, nothing is more important than security, privacy and control. 
 Here’s how we ensure it:

the next produced token will be. Temperature, top-K, and top-P are the most common configuration settings that determine how predicted token probabilities are processed to choose a single output token sampling)4 are two sampling settings used in LLMs to restrict the predicted next token to come from tokens with the top predicted probabilities. Like temperature, these sampling settings control the randomness and desired outcome, and the settings all impact one another. It’s also important to make sure you understand how your chosen model combines the different sampling settings together. If temperature, top-K

Microsoft, Google, Anthropic, Meta, Apple, Alibaba, Deepseek, UK Government, US Department of Homeland Security. China data may be subject to informational limitations due to government restrictions. 3/23: AI assistant focused on safety & inter- pretability 3/24: USA Department of Homeland Security unveils its AI Roadmap Strategy 5/24: OpenAI releases GPT-4o, which has full multimodality technology for 10,000 physicians and staff to augment their clinical capabilities across diverse settings and specialties. - New England Journal of Medicine Catalyst Research Report, 2/24 Unique Kaiser

extensive and intricate rulesets, making updates costly or error-prone, 
 for example performing vendor security reviews. 03 Heavy reliance on unstructured data: Scenarios that involve interpreting natural language robust authentication and authorization protocols, strict access controls, and standard software security measures. 24 A practical guide to building agents Think of guardrails as a layered defense mechanism Add new guardrails based on real-world edge cases and failures you encounter 03 Optimize for both security and user experience, tweaking your guardrails as your
 agent evolves. 27 A practical guide to building

introduce our pre-training endeavors, including the training data construction, hyper-parameter settings, infrastructures, long context extension, and the evaluation of model performance and efficiency normalization and the activation function in FFNs), unless specifically stated, DeepSeek-V2 follows the settings of DeepSeek 67B (DeepSeek-AI, 2024). 2.1. Multi-Head Latent Attention: Boosting Inference Efficiency set the scale ? to 40, ? to 1, ? to 32, and the target maximum context length to 160K. Under these settings, we can expect the model to respond well for a context length of 128K. Slightly diverging from original