Building a production chatbot requires more than prompt engineering. You need models that maintain coherence across long sessions, execute tools reliably, and remain cost predictable as conversation depth grows. This guide breaks down the technical requirements for conversational AI, from model selection to memory architecture, and shows how to implement a robust system using modern inference platforms.
Core Requirements for Conversational LLMs
Conversational AI imposes constraints that differ from single-turn text generation. A chatbot must handle multi-turn state, follow system instructions across dozens of exchanges, and return structured data for downstream actions.
- Context retention. Sessions often exceed 8K tokens. Models with 128K or 1M context windows reduce the need for aggressive truncation.
- Tool use. Function calling lets bots query APIs, update databases, or trigger workflows without fragile regex parsing.
- Latency. Streaming tokens improves perceived responsiveness, especially for long-form replies.
- Structured output. JSON mode ensures bot responses parse cleanly into your application logic.
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