We solemnly promise that each bit of content you read here
is thoughtfully curated or crafted by a human, for a human. We spend hundreds of hours every month speaking to experts with decades of experience. Our blogs are more a journalistic report on the state of experimentation than a single perspective article. Enjoy!
Last time, we created a research workflow. This workflow connected to a spreadsheet containing our user feedback, then randomly sampled it and extracted relevant themes. We used a small model, so the entire workflow was highly energy-efficient. Wouldn’t it be nice if we could quantify the themes by counting...
We have a spreadsheet full of user feedback. We’ve put them all into a vector store, and now we can search the feedback semantically using theme labels, specific mentions, etc. (as we did in previous videos). But there’s a problem. This workflow still requires us to familiarise ourselves with...
How do you create effective workflows that deal with large volumes of user text data? Like, say you want an LLM to analyse large volumes of user feedback for insights as a workflow. In this instance, what you need to learn is a process called Retrieval Augmented Generation (RAG)....
We’ve done quite a bit in n8n, but we haven’t really created any meaningful connections. And by meaningful connections, I mean connecting to the likes of Google, Slack, Airtable, or other tools you might be using. So far, we have a manual process for entering user text into a...
I don’t know about you, but I get bored manually trudging through an interaction with an LLM. We say something, it says something back, and on and on, until we get what we need. Worse still, in previous steps we took a single interaction with ChatGPT to analyse user...
Do you have ideas about what you want to do with AI, but just don’t know where to start? Or you don’t even know what you can do with AI in the first place. What both scenarios have in common is a lack of understanding of AI’s capabilities beyond...
