Prompt Engineering

I’ve been working with large language models from across the spectrum, including OpenAI GPT, Anthropic, LLAMA, etc., for quite some time. For most of this journey, I was leaning towards selecting larger models (with more context windows) and cramming as much context as possible in hopes of getting better inferences and domain-specific reasoning. However, after the release of GPT-3.5-Turbo-16K, I realized the capabilities of these LLMs do not necessarily scale with the context window....

In this blog, we will discuss prompt engineering techniques used in working with large language models: zero-shot, one-shot, and few-shot learning. These methods aim to enable models to perform and learn new tasks quickly with little or no training data. Zero-Shot Learning Zero-shot learning refers to the ability of a language model to perform a task without having seen any examples from that specific task during training. This is particularly useful when there is a lack of labeled data for a given task....