✳flâneur — a map of the web's best reading

Amelia Wattenberger

wattenberger.com · 882 words · saved by 1 readers

When we talk about adding context to AI-enhanced interfaces, we’re usually focused on feeding LLMs the right information. Writing the best prompts, connecting to data stores, that kind of thing. But what about providing context to us, the human users? Let's look at the typical back-and-forth with a chatbot: you ask something, it responds, and so on. Say you’ve heard about a fun new concept called condensation and you want to learn more about it. This is a great answer! We've learned what condensation is, when it happens, and even got an example. What would be wonderful, though, is showing common frameworks that this knowledge is usually presented with. If we look at the Wikipedia entry for condensation, we see this lovely table: Looking at this table, I can quickly see what the phases are, what the other transitions are called, and even that the transition between phases is different depending on its direction. And on top of that, I can very easily continue down the rabbit hole by clic

Putting knowledge in its place When we talk about adding context to AI-enhanced interfaces, we’re usually focused on feeding LLMs the right information. Writing the best prompts, connecting to data stores, that kind of thing. But what about providing context to us, the human users ? Let's look at the typical back-and-forth with a chatbot: you ask something, it responds, and so on. Say you’ve heard about a fun new concept called condensation and you want to learn more about it. This is a great answer! We've learned what condensation is, when it happens, and even got an example. What would be wo

Explore this link on the map →

related reading