Hashtags were a defining innovation of Web 2.0; what started as a user-invented hack on Twitter in 2007 has become entrenched as an organizing tool in platforms like Instagram and TikTok, driving community curation and content discovery. This was “folksonomy” in action: bottom-up labeling that adapts faster than top-down taxonomies ever could. But with #AI, can we reinvent the concept of taxonomy to combine the “evolveability” of a bottom-up approach with the “systemizability” of a top-down approach? That is the promise of topic modeling.

Retrieval-Augmented Generation (RAG) pipelines have revolutionised how we integrate custom data with large language models (LLMs), unlocking new possibilities in AI applications. However, evaluating the effectiveness of these pipelines has presented a major challenge for most real-world applications. In this post, we’ll look deeper into the pain points of RAG pipeline evaluation and explore strategies to overcome them.

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