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6/17/2026
Building Effective Dialogue Systems with LLMs

Building Effective Dialogue Systems with LLMs

Short summary

Production dialogue systems require stateful architecture with careful model selection for your domain and strategic tool use. Long-context models (Kimi K2.6, DeepSeek V4) and request-based pricing can reduce costs 10–100x versus token-based alternatives. Code examples show conversation loops, function calling, and context management patterns.

  • •Dialogue state management: persist history in Redis/database, truncate or summarize when approaching context limits
  • •Model selection: match domain (Llama for support, Qwen for agents, Kimi/DeepSeek for long context, GLM for agentic tasks)
  • •Tool use via OpenAI SDK: function calling enables deterministic handoffs between NLP and backend APIs

Generated with AI, which can make mistakes.

#ai-tools#ai-agents
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The original title is "Three foundational concepts for production LLM applications"
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