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Developers, Your RAG Support Chat Bot Needs Analytics

Introduction


Developer-facing companies... PLEASE STOP blindly implementing a RAG solution to handle support in your Slack, Discord, or Discourse forum.


Over the last few months there's a been a huge trend in implementing RAG for technical documentation and offloading support. In theory this makes sense to free up developers on rotation, but it's not as "simple" as you think.


You need to have some sort of analytics behind your bot, decoupling queries from bot responses and synthesizing this information to continue extracting value from the voice of the customer. Otherwise, you're screwing them and yourself in the long-term.


Power Law, And Scale Invariance


There's an inverse, non-linear relationship between the number of queries your bot answers and the amount of knowledge you gather. This relationship follows the Power Law Curve (a more in-depth review can be found here).


Power Law Curve

Chat-like experiences promote interaction, resulting in common themes and questions quickly emerging at a greater velocity than before. The more the bot handles, the less you know about your customers, and it's all trapped in a word-soup of nested bot interactions.



Knowledge vs Chatbot creates a Power Law distribution

The end result of this phenomenon means: 


  • You lose out on finding common roadblocks or issues developers are experiencing

  • You lose out on extracting new feature ideas from common interactions 

  • You lose out on identifying new information to include in your docs based on commonly asked questions


You're shooting yourself in the foot. 


The Optimal RAG Solution


The best way to implement this method is to have a robust solution behind it that's capable of extracting all of this information at speed, scale, and accuracy. This is what we're building at Siftree, an intelligence platform with state-of-the-art natural language processing at its core, delivering real-time insights on behalf of your community or audience.


Siftree identifies both commonalities and anomalies across all interactions, surfacing highly relevant information, and acts as the spokesperson for the voice of the customer/community.


Without a powerful engine analyzing your support-bot's interactions, you're going to end up blinding yourself to the needs of your community and limit the value you can extract from their interactions.

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