When Anthropic introduced Claude Skills, they demonstrated how specialized instruction sets could transform AI assistants into domain experts. But not every developer can use Claude at work. Many companies have IT restrictions or security policies that prevent the use of a large LLM like Anthropic's. Or they may just want to avoid context-switching between a million different AI tools. Count me as one of those devs.
AI context as a reflection of human memory To make a large language model (LLM) truly useful, everything begins with context management. Context is more than a single prompt, it is the entire accumulation of conversation series. Every user input, combined with every AI response, forms the evolving environment in which meaning is created.
Data and AI were the dominant martech buzzwords in early 2025, but context is taking their place as the year wraps up. This isn't entirely new; more than seven years ago, a MarTech article named content as king and context as queen. But in the age of AI, the volume of data and content flowing into systems is bringing context back to the forefront. Context is what takes data and turns it into something more valuable, like relationships and experiences.
