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S&P Global Market Intelligence and P Global Market Intelligence द्वारा प्रदान की गई सामग्री. एपिसोड, ग्राफिक्स और पॉडकास्ट विवरण सहित सभी पॉडकास्ट सामग्री S&P Global Market Intelligence and P Global Market Intelligence या उनके पॉडकास्ट प्लेटफ़ॉर्म पार्टनर द्वारा सीधे अपलोड और प्रदान की जाती है। यदि आपको लगता है कि कोई आपकी अनुमति के बिना आपके कॉपीराइट किए गए कार्य का उपयोग कर रहा है, तो आप यहां बताई गई प्रक्रिया का पालन कर सकते हैं https://hi.player.fm/legal
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Context Engineering

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S&P Global Market Intelligence and P Global Market Intelligence द्वारा प्रदान की गई सामग्री. एपिसोड, ग्राफिक्स और पॉडकास्ट विवरण सहित सभी पॉडकास्ट सामग्री S&P Global Market Intelligence and P Global Market Intelligence या उनके पॉडकास्ट प्लेटफ़ॉर्म पार्टनर द्वारा सीधे अपलोड और प्रदान की जाती है। यदि आपको लगता है कि कोई आपकी अनुमति के बिना आपके कॉपीराइट किए गए कार्य का उपयोग कर रहा है, तो आप यहां बताई गई प्रक्रिया का पालन कर सकते हैं https://hi.player.fm/legal

As organizations have worked to leverage the power of AI in interacting with large language models, they've invested in prompt engineering to generate better results. But agents shift the need manage the full context of not only the prompt, but also the data that's being presented. Analysts Jean Atelsek and Alex Johnston return to the podcast to look at the new discipline of context engineering and how it's being put to work in AI environments with host Eric Hanselman. The process of context engineering looks at ensuring that the right data context is in place for agents to act on. It requires a shift from thinking that more data is necessarily better and understanding to getting the right data is the best insurance against agents picking up bad habits. We've come full circle in approaches to data and organizations need to raise the level of abstraction at which they address data need for agentic applications.

We've been working through waves of capability in the march to agentic operations. Organizations have access to the same models, but how they're used is where differentiation is possible. Agentic approaches demand greater sophistication and understanding around the context with which data is presented to applications. There has to be more careful curation, to get reasonable results.

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100 एपिसोडस

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Context Engineering

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Manage episode 522522291 series 2877784
S&P Global Market Intelligence and P Global Market Intelligence द्वारा प्रदान की गई सामग्री. एपिसोड, ग्राफिक्स और पॉडकास्ट विवरण सहित सभी पॉडकास्ट सामग्री S&P Global Market Intelligence and P Global Market Intelligence या उनके पॉडकास्ट प्लेटफ़ॉर्म पार्टनर द्वारा सीधे अपलोड और प्रदान की जाती है। यदि आपको लगता है कि कोई आपकी अनुमति के बिना आपके कॉपीराइट किए गए कार्य का उपयोग कर रहा है, तो आप यहां बताई गई प्रक्रिया का पालन कर सकते हैं https://hi.player.fm/legal

As organizations have worked to leverage the power of AI in interacting with large language models, they've invested in prompt engineering to generate better results. But agents shift the need manage the full context of not only the prompt, but also the data that's being presented. Analysts Jean Atelsek and Alex Johnston return to the podcast to look at the new discipline of context engineering and how it's being put to work in AI environments with host Eric Hanselman. The process of context engineering looks at ensuring that the right data context is in place for agents to act on. It requires a shift from thinking that more data is necessarily better and understanding to getting the right data is the best insurance against agents picking up bad habits. We've come full circle in approaches to data and organizations need to raise the level of abstraction at which they address data need for agentic applications.

We've been working through waves of capability in the march to agentic operations. Organizations have access to the same models, but how they're used is where differentiation is possible. Agentic approaches demand greater sophistication and understanding around the context with which data is presented to applications. There has to be more careful curation, to get reasonable results.

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