Conversational AI systems now power customer support, internal research agents, and autonomous coding assistants. A production deployment requires managing multi-turn context, tool execution, streaming latency, and cost controls that scale with usage. If your infrastructure bills per token, long conversations and document-heavy prompts can quickly distort your budget. This guide covers the architectural decisions and implementation patterns that separate prototype chatbots from reliable conversational backends.
Design the Conversation State
Every message in a dialogue adds tokens to your context window. In production, you must decide how to truncate, summarize, or compress history before you exceed model limits or burn through inference budget. A common pattern is to maintain a sliding window of the last N turns, plus a condensed summary
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