y] * B[k, x], axis=k)) Computation Specification (Tensor Expression) A = tvm.placeholder((8, 8)) B = tvm.placeholder((8,))
 k = tvm.reduce_axis((0, 8)) C = tvm.compute((8, 8), 
 lambda y, x: tvm

between different pieces of data and even make the LLM "time-aware" by including date or timestamp fields with specific formats. Here's a simple example: Let's say you want to use an LLM to generate descriptions relevant description. This structured input approach, guiding the LLM's attention to the relevant fields, is especially valuable when working with large volumes of data or when integrating LLMs into complex prompt performance on different versions of a model, and to help debug future errors. Beyond the fields in this table, it’s also helpful to track the version of the prompt (iteration), a field to capture