We've deployed over 20 customer-facing chatbots in the past three years. Some have become the most-used support channel their clients have. Others were quietly switched off after a few weeks. The difference isn't the underlying AI model — it's almost always in the design and deployment decisions that surround it.
Define a Precise Scope
The chatbots that fail are almost always the ones that were asked to do too much. "Handle all customer queries" is not a scope — it's a wish. Define exactly which queries the bot should handle, which it should escalate, and which it should decline entirely.
A bot that handles 10 things brilliantly is worth ten times a bot that handles 100 things badly.
Invest in the Knowledge Base
LLM-powered bots with retrieval-augmented generation (RAG) are only as good as the documents they retrieve from. Garbage in, garbage out. Before launch, audit every piece of content the bot will reference. Is it accurate? Is it current? Is it written in a way that maps to how users ask questions?
Knowledge base maintenance is not a one-time task. Budget for it as an ongoing operational cost.
Design the Handoff First
The most critical user experience in any chatbot isn't the successful self-service resolution — it's the moment the bot can't help and needs to pass to a human. If this handoff is clunky, users will remember it forever. If it's seamless — full context transferred, no need to repeat information — they'll barely notice it happened.
Design the handoff journey before you design anything else.
Measure What Matters
Containment rate (% of queries resolved without human intervention) and CSAT are your two north stars. Everything else — intent accuracy, response time, session length — is a diagnostic metric that helps you improve those two numbers.
