Meaning, meet action! Why context engineering needs context
The Large Language Models (LLMs) driving recent progress in AI can do many interesting things, and sometimes well. Increasingly, the industry is recognizing that it can get better results by narrowing the focus for a given question or task to the specific supporting context. The reality on the ground is more nuanced because there are, well, different contexts to context.
I recently caught up with a couple of vendors exploring this question from different facets: meaning and action. And their approaches to thinking about context engineering and orchestrating the workflows to support it differ in some interesting ways.
- Meaning seems to be about reducing the amount of information you surface to a model required to answer a question, run analytics, or inform a more helpful decision.
- Action is about thinking about how to reduce the instructions you might give an agent to reduce its scope of action to the minimum...
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