Prompt Engineering and LLMs with Langchain

We have always relied on different models for different tasks in machine learning. With the introduction of multi-modality and Large Language Models (LLMs), this has changed.

Gone are the days when we needed separate models for classification, named entity recognition (NER), question-answering (QA), and many other tasks.

This is a companion discussion topic for the original entry at

A point that wasn’t discussed in the chapter is the token consumption of prompt templates. Is there a way of making the templates less token consuming without losing accuracy (other than rephrasing the template)?