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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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Credits:
100 episod
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.
More S&P Global Content:
For S&P Global Subscribers:
Credits:
100 episod
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