Angelina YangFounder and Host, TwoSetAI · AI builder for visibility and discoverability

The Power of Context Engineering in AI Systems

16 Oct 2025 · 1 min

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0:45

Speaker A introduces and endorses the concept of "context engineering" as a strategy to improve model accuracy by bridging gaps between knowledge, memory, and decision-making. They highlight extending the context window and restructuring data as practical approaches, and raise questions about which parts of context need to be fed to models versus supplying subsets.

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Context engineering term is very interesting to me, and I think it's going toward the right direction to what models need to do that, how to give that information so that models are more accurate. In the end, you should probably try in your projects to remove those bottleneck pipelines and try to bridge the gap between the knowledge, the memory, and where the decision is made. If you can bridge the gap between both, the one way to extend this context window is a way to do that, and especially if you structure your data another way, because context window very useful. There is something else related to how you bring data to your models, what type of architectures you are using. Do you need the whole context to feed to your system every time, or you just need to give it a subset of it.