Context Engineering: Beyond Prompting in AI
The AI landscape is rapidly evolving, shifting focus from mere “prompt engineering” to the more comprehensive “context engineering.” This emerging discipline emphasizes providing Large Language Models (LLMs) with the precise information and tools, at the opportune moment, to ensure successful task completion. The quality of this contextual input is now recognized as the primary determinant of an AI agent’s effectiveness, moving beyond the limitations of simple prompts and highlighting that most agent failures are, in fact, context failures.
Points clés
- Tobi Lutke defines Context Engineering as “the art of providing all the context for the task to be plausibly solvable by the LLM.”
- The success or failure of AI Agents is primarily determined by the quality of the context provided, not just the model itself.
- Context encompasses various elements: Instructions/System Prompt, User Prompt, State/History (short-term memory), Long-Term Memory, Retrieved Information (RAG), Available Tools, and Structured Output.
- A “Magical” AI Agent’s effectiveness stems from its ability to gather and utilize rich context, such as calendar information, past emails, and contact lists, rather than complex code.
- Context Engineering is a dynamic system that generates context on the fly, tailored to the immediate task.
- The discipline emphasizes providing the right information and tools at the right time, adhering to the “Garbage In, Garbage Out” principle.
- The format of information presented to the LLM is crucial for effective context engineering.
- Philipp Schmid highlights that building powerful AI Agents is less about finding a magic prompt and more about engineering the context.
- Key figures like Tobi Lutke and Karpathy have contributed to the discourse around context engineering.
À retenir
So, you thought you were hot stuff with your perfectly crafted prompts, did you? Well, it turns out that’s just kindergarten stuff now. The real gurus are busy “engineering context,” which sounds suspiciously like making sure your AI has all the juicy gossip and relevant spreadsheets before it even thinks about answering. Apparently, if your AI messes up, it’s not its fault; it’s because you didn’t give it enough background info. So, next time your AI bot gives you a robotic, unhelpful answer, remember, it’s not dumb, it’s just poorly informed. Time to get your context game on, or risk being left in the digital dust with your “cheap demo” AI!
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