perception, but for path planning and vehicle controls. We replaced 330,000 lines of C++ code with neural nets. It's really quite remarkable. So, as a side note, I think Tesla is probably the most probably

of this paper, we first provide a detailed description of the model architecture of DeepSeek-V2 (Section 2). Subsequently, we introduce our pre-training endeavors, including the training data construction infrastructures, long context extension, and the evaluation of model performance and efficiency (Section 3). Following this, we demon- strate our efforts in alignment, encompassing Supervised Fine-Tuning results, and other discussion (Section 4). Finally, we summarize the conclusion, deliberate on the current limitations of DeepSeek-V2, and outline our future work (Section 5). 2. Architecture By and large

of tracking prompt engineering work, and the prompt development process is in the Best Practices section later in this chapter (“Document the various prompt attempts”). The model temperature should be into further detail on CoT prompting: Prompt Engineering February 2025 32 In the best practices section of this chapter, we will learn some best practices specific to Chain of thought prompting. Self-consistency overcome solely by increasing model size. As we learned in the previous Chain of Thought prompting section, the model can be prompted to generate reasoning steps like a human solving a problem. However CoT

strengths and tradeoffs related to task complexity, latency, and cost. As we’ll see in the next section on Orchestration, you might want to consider using a variety 
 of models for different tasks in the Agents themselves can serve as tools for other agents—see the Manager Pattern in the Orchestration section. Refund agent, Research agent, Writing agent. 9 A practical guide to building agents For example