The State of Context Engineering
This article discusses the emerging field of 'context engineering' in AI, focusing on the challenges of providing consistent instructions to large language models. It argues that despite advancements in model capabilities, the difficulty of managing context remains a persistent hurdle for developers.
Why it matters
As AI models become more integrated into workflows, understanding how to effectively prompt and manage context is critical for reliable performance.
This week, I gave a short talk at AGNTCon titled, “Context Fails & Fixes,” that functioned as a sort of “State of Context Engineering.” How did context engineering emerge, where has it gone, and where might it go in the future? As model makers target new capabilities, they inadvertently create challenges we, as users and harness builders, must overcome.
Below are the slides and a lightly edited transcript. As always, shoot me a note with any feedback, thoughts, or questions . I really appreciate it.
Hi, everyone. Thanks for joining me today. We’re going to walk through context fails and fixes.
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