Text classification is one of the most useful applications of Large Language Models (LLMs). They can classify text using a small number of examples (few-shot classification).
LLMs can write coherent text like no other human technology before them could. This can be used for creative copy, but also for summarization and paraphrasing. We tune the inputs using prompt engineering techniques that get the model to produce useful outputs. Important text generations parameters include top-k and top-p.
Learn how to use embeddings to build semantic search capabilities.
Extract information from text using only a few examples.
Learn how the Likelihood endpoint can be a useful tool for model evaluation.
See the notebooks repo for code examples on common LLM use cases.
The Cohere platform is often used in pipelines alongside other tools and services.