Hi HN,<p>This is the first in a series of articles I'm writing to introduce devs to practical applications of large NLP language models (for text generations like GPT and for language understanding like BERT).<p>I have been connecting the dots between the capabilities of these models and their business application. I still believe we're in the beginning of grasping the amount of potential value we can extract from these models. Happy to get to share these as I learn them from my exposure to the problem space.<p>Some of the key visual language I'm aiming to simplify is that of "prompts" and their use to shape model output (leading to practical applications). In this post, a key visual is [1] which shows an example of a summarization prompt and [2] showing a high-level process of "prompt engineering".<p>Would appreciate your feedback!<p>[1] <a href="https://docs.cohere.ai/img/intro-llms/language-model-prompt.png" rel="nofollow">https://docs.cohere.ai/img/intro-llms/language-model-prompt....</a>
[2] <a href="https://docs.cohere.ai/img/intro-llms/prompt-engineering-and-finetuning.png" rel="nofollow">https://docs.cohere.ai/img/intro-llms/prompt-engineering-and...</a>