I’m working on an AI chat application that lets people upload documents like PDFs and Word files to create personalized chatbots using retrieval augmented generation.
When users ask follow-up questions, I modify their input based on previous conversation context before retrieving relevant information and generating answers. This approach requires two API calls but handles follow-up queries well.
The issue occurs when users send casual messages instead of actual questions. Things like greetings, acknowledgments, or simple responses don’t need the full RAG pipeline. Currently my system treats everything the same way, which makes the bot give weird responses to casual input.
How can I identify whether user input needs the complete retrieval process or just a simple conversational response? Looking for practical approaches to handle this classification step.
Been fighting this same issue for months on my customer service RAG system. Here’s what actually worked: First, I check input length - anything under 10 characters goes straight to conversational handling. Then I run a quick semantic similarity check against common casual phrases I keep in a small database. Works way better than expected. The real breakthrough was analyzing conversation flow. If my bot just gave a definitive answer and the user responds with short stuff like “thanks” or “ok,” I skip retrieval completely. Context beats actual words half the time. Don’t make my mistake though - don’t rely on just question words. Users ask real questions as statements all the time: “I need to understand the refund policy.” Basic keyword filters miss these completely. Now, I combine three things: message length, conversation state, and a lightweight BERT classifier that runs under 100ms. Make the decision fast and fall back to full RAG when you’re not sure. Better to over-retrieve than miss a real question.
I hit this same issue building a support bot for our internal docs. The double API calls were destroying response times on basic interactions.
I threw a lightweight classifier in front of the RAG pipeline. Trained a simple model to sort inputs into three groups: questions, greetings, and acknowledgments.
Start with keyword detection for quick wins. Hunt for question words - what, how, why, when, where. Flag greeting patterns like hi, hello, thanks, got it.
Here’s what actually moved the needle - I changed how I used conversation context. User just dropped a “thanks” or “okay” after the bot answered something? Skip RAG completely. Same for opening greetings.
This trick saved me endless pain: run a simple intent classifier next to your main system. Small transformer model works, even regex for common stuff. Adds maybe 50ms but you dodge the full retrieval cycle.
Make the classification fast and reliable. Don’t over-engineer it, but don’t let “thanks” trigger document searches either.