That’s where “chain of thought” prompts come in you give them a series of steps to follow and they work through the problem like a human would.

But here’s the thing, these LLMs are super slow when it comes to actually doing stuff. So we want to optimize their performance by using NVIDIA TensorRT-LLM (which is basically a fancy way of saying “we’re gonna make them run faster on our graphics cards”).

Here’s how it works: first, you take your LLM and break it down into smaller pieces called layers. Then you apply some math magic to those layers so they can work more efficiently (like layer normalization or alternative attention schemes). Finally, you use TensorRT-LLM to optimize the inference process by reducing memory usage and improving throughput.

For example, let’s say we have an LLM that needs to figure out what time it is based on a given text input. Instead of just spitting out the answer right away (which would be boring), we can use a chain-of-thought prompt to guide them through the steps:

1. Identify any relevant time information in the text (e.g., “today at 3pm”)
2. Convert that information into a format that can be easily processed by the LLM (e.g., “03:00 PM”)
3. Calculate the current time based on the input and convert it to the desired output format (e.g., “The current time is 15:42 PM.”)

By using this chain-of-thought prompt, we can help the LLM understand how to solve the problem more accurately and interpretably. And by optimizing their performance with TensorRT-LLM, we can make them run faster on our graphics cards (which is a huge deal when you’re dealing with massive amounts of data).

And if that doesn’t make sense, just remember: it’s like giving your computer a brain transplant (but without the actual surgery part).