Runtime Property Registr \L Compiler Toolchain 于 TVM TOPI Schedule Primitives & Optimizations Symbols NNVM & Param Frontends Operators Algorithm &Schedule CUDA TOPI Backends Machine Learning

prompts. In Table 1, you will see an example zero-shot prompt to classify movie reviews. The table format as used below is a great way of documenting prompts. Your prompts will likely go through many iterations keep track of your prompt engineering work in a disciplined, structured way. More on this table format, the importance of tracking prompt engineering work, and the prompt development process is in the prompt to return a certain structure. Have a look into Table 4, where I return the output in JSON format. Prompt Engineering February 2025 20 Goal Classify movie reviews as positive, neutral or negative

IRModule([te_add_one]) print(mod[”te_add_one”].args) Use hybrid script as an alternative text format Directly write pass, manipulate IR structures Accelerate innovation, 
 e.g. use (GA/RL/BayesOpt/your

trends shown in this data? 找趋势 Can you describe the data? 描述数据 Show me the top trends in a visual format. 以视觉形式显示趋势 Can you clean this dataset? 清洗数据 Can you create a heatmap using this data? 创建一个热力图 Can

it is worth noting that chat models, such as LLaMA3 70B Instruct, might not strictly adhere to the format constraints typically specified in the few-shot setting. Consequently, this can lead to underestimation

ability to mimic human conversation. In this study, ~500 participants engaged in a three-party test format, interacting with both a human and an AI. Most discussions leaned on emotional resonance and day-to-day