multi-turn conversations

**Multi-turn conversations** is the **dialogue mode where responses depend on prior interaction history across multiple user-assistant exchanges** - effective handling requires explicit state management because model calls are stateless by default. **What Is Multi-turn conversations?** - **Definition**: Conversational interaction pattern in which context accumulates over sequential turns. - **State Requirement**: Prior messages must be supplied or summarized for each new model call. - **Context Scope**: Includes user goals, constraints, corrections, and unresolved references. - **Failure Risk**: Missing history leads to incoherent answers, repetition, or lost task continuity. **Why Multi-turn conversations Matters** - **User Experience**: Consistent memory across turns is essential for natural dialogue quality. - **Task Completion**: Complex workflows often require iterative refinement rather than one-shot answers. - **Context Integrity**: Accurate carry-forward of prior constraints reduces instruction drift. - **Operational Complexity**: Conversation growth can exceed context window and increase latency cost. - **Product Differentiation**: Strong multi-turn handling is a major quality signal in assistant systems. **How It Is Used in Practice** - **History Policy**: Decide what to retain verbatim, summarize, or retrieve on demand. - **Reference Resolution**: Track entities and commitments to support pronoun and follow-up understanding. - **Memory Guardrails**: Prevent stale or conflicting historical instructions from dominating current intent. Multi-turn conversations is **a foundational interaction mode for production assistants** - robust dialogue-state handling is required to maintain coherence, efficiency, and trust across extended sessions.