In all my years of technology reporting, one thing hasn’t changed: Data remains an organizational headache.

The AI era introduces a specific hurdle of making data legible for large language models. As TNS Senior Editor for AI Frederic Lardinois reports, “massive tables and multidimensional JSON files” aren’t enough. Without the original business context, AI simply can’t reason over the results.

On Thursday, Precog launched a feature designed to bake context directly into the data extraction process. Frederic explains how the new feature puts the business context back in data.

In other posts of interest:

• In our frontend coverage, TNS Senior Editor Richard MacManus explores why platform companies such as Cloudflare buy web frameworks like Astro.
• In open source news, TNS Senior Editor Darryl Taft reports on a new, customer-led steering committee for the Chainguard OS.

As AI coding assistants become more capable, the relationship between developers and their tools is evolving beyond a simple autocomplete. AI won’t replace developers, but we need to rethink the way developers work with AI to maximize speed and quality. One highly effective approach is what I call the "head chef" model. Much like a head chef doesn't chop every vegetable or stir every pot, developers...

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