Viewing enriched Twitter post
We're all in on context engineering! A related topic that imo is table stakes for every AI engineer/user: workflow engineering 🛠️ A lot of agent use cases revolve around automating work that otherwise a human would have to perform - customer support, legal research, report… https://t.co/Ry2F1IapZp
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"text": "We're all in on context engineering! \n\nA related topic that imo is table stakes for every AI engineer/user: workflow engineering 🛠️\n\nA lot of agent use cases revolve around automating work that otherwise a human would have to perform - customer support, legal research, report generation, unit testing, etc. \n\nIt obviously needs to be context-aware, but it also needs to be a repeatable multi-step process; there's usually a sequence of steps that you can describe that require multiple LLM calls to achieve a given outcome. \n\nIn most cases you can't fully trust a simple ReAct agent with tools - it is too unconstrained and doesn't necessarily fulfill the task at hand. \n\nBoth AI engineers and non-technical users need to get really good at describing these workflows, because that's how you get AI to meaningfully complete work for you instead of giving you back chat responses from the raw LLM API. \n\n(If you go back to the model layer, you could even use RL/tuning to optimize/overfit to the workflow at hand, to ensure 100% accuracy vs. 80%) \n\nI'm super bullish on workflow engineering; at the end of the day every AI builder is building specialized AI workflows in one way or the other. We're building document workflows at @llama_index, other companies are building customer support workflows, coding workflows, and more.",
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