I keep seeing teams swap models hoping smarter AI will fix their chatbot, but the real failure mode is almost always structure, not intelligence. A single-prompt chatbot with no memory, no retrieval discipline and no notion of workflow will fall apart the moment a conversation spans multiple turns, departments or constraints. We ran into this with a mid-size SaaS company whose support bot worked fine for FAQs, but completely collapsed when users asked things like upgrade my plan, apply last month’s credit, and explain why my invoice changed. The bot knew the words it just didn’t know how to reason through the process. What finally worked was treating the system less like a chatbot and more like a conversation-driven service. We split responsibilities: one component to interpret intent, another to fetch verified context (plans, billing rules, user state) and a thin reasoning layer that only answers when evidence is present. Suddenly the same model produced far more reliable answers, because it wasn’t guessing anymore. The big shift was accepting that complex business conversations are really multi-step workflows disguised as chat. If you’re struggling with a bot that sounds fluent but makes bad decisions, you probably don’t need a bigger model you need clearer state, better retrieval and explicit guardrails. Happy to guide anyone working on this.
submitted by /u/Safe_Flounder_4690
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