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There has been a recent surge in Visual Programming tools which enable developers to chain large language model prompts into an application, thus creating a conversational user interface.

In previous articles, I argued that a data-centric approach should be taken when engineering training data for Natural Language Understanding (NLU). Building on this, this article will discuss the importance of creating and using intents when working with Large Language Models (LLMs).

Reaching targeted levels of intent recognition can be quickly achieved by verifying intents before deployment, rather than adopting a corrective strategy as a response.

The notion to create workflows (chains) which leverage Large Language Models (LLMs) are necessary and needed. But there are a few considerations, one of which is Prompt Drift.