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I recently worked on improving chunking strategies for a Slack text RAG (Retrieval-Augmented Generation) system, and I wanted to share my approach — especially for those dealing with chaotic, real-world conversational data. When you’re trying to retrieve relevant context from Slack conversations, naive chunking can lead to fragmented or unhelpful responses. So I combined three different chunking strategies to make the data much richer and improve retrieval quality. By doing this, I saw about a 5–6% increase in accuracy, and interestingly, the system gets even more accurate as more data is added.

Apr 6, 2025 - 06:23

I recently worked on improving chunking strategies for a Slack text RAG (Retrieval-Augmented Generation) system, and I wanted to share my approach — especially for those dealing with chaotic, real-world conversational data.

When you’re trying to retrieve relevant context from Slack conversations, naive chunking can lead to fragmented or unhelpful responses. So I combined three different chunking strategies to make the data much richer and improve retrieval quality.

By doing this, I saw about a 5–6% increase in accuracy, and interestingly, the system gets even more accurate as more data is added.