Building an Intent Classifier to Route Messages Across Multiple LLMs
The article discusses the author's approach to building an intent classifier that can route user queries to the most suitable Large Language Model (LLM) for the task, rather than relying on a single model for everything.
Why it matters
This approach addresses a key limitation in current AI chat apps and demonstrates how to build a more capable and flexible system by leveraging multiple LLMs.
Key Points
- 1Single-model architectures in AI chat apps create friction for users and limit capabilities
- 2The author's solution is a rule-based intent classifier written in JavaScript to route queries to the best-suited LLM
- 3The classifier categorizes queries into 7 types: chitchat, coding, reasoning, creative, search, factual, and general
Details
The author explains that most AI chat apps assume a single LLM is sufficient for all user queries, which is not the case. Different LLMs excel at different tasks, such as code generation, multi-step reasoning, or live web search. To address this, the author built a routing layer that can determine the intent of a user's query and send it to the most appropriate LLM. The initial approach of using an LLM for intent classification was abandoned due to unacceptable latency. Instead, the author developed a rule-based classifier written in JavaScript that runs synchronously before any request is sent out. The classifier categorizes queries into 7 types: chitchat, coding, reasoning, creative, search, factual, and general. This allows the system to leverage the unique strengths of different LLMs to provide the best possible response to the user.
No comments yet
Be the first to comment