Conversational AI — what it actually takes to build one
Most people underestimate how much architecture goes into a working conversational AI system before a single user ever types a message.
We sat down with one of Cerevyny's senior mentors to talk through the real decisions, common mistakes, and what separates a chatbot from a system that actually holds a conversation.
Questions from the community
These questions came directly from people enrolled in Cerevyny's mentorship track. The answers reflect real patterns observed across dozens of client projects — not theory.
The hardest part of building a conversational AI is not the model — it is deciding what the system should refuse to do, and enforcing that consistently across every turn.
Where do most people go wrong when they start building a conversational AI for the first time?
They start with the interface. They spend two weeks on a chat widget before they have a single intent mapped. The dialogue logic — what the system understands, what it responds to, how it handles ambiguity — needs to exist before any UI does. Skipping that step creates systems that look finished but break on the third user message.
How do you handle context across a long conversation without the system losing track?
Context management is a design problem more than a technical one. You have to decide what the system needs to remember, for how long, and what it should actively forget. A session state object with explicit slots — topic, user preference, last confirmed entity — beats relying on raw conversation history every time. Raw history grows fast and introduces noise.
When does it make sense to use a large language model versus a rules-based approach?
Rules-based systems are underrated for structured domains — booking, support triage, form completion. They are predictable and auditable. LLMs become necessary when the range of valid inputs is too wide to enumerate, or when the response needs to feel genuinely adaptive. Many production systems use both: rules for routing, an LLM for generation within a constrained output space.
What does a realistic evaluation process look like before going live?
You need at least three test sets: happy path conversations, edge cases you designed for, and adversarial inputs you did not design for. The third set is the one most teams skip. Someone on your team should spend an afternoon trying to break the system before any user touches it. Automated metrics like intent accuracy tell you one thing — real failure modes tell you another.
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