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How to Improve Context Retention in Multi-Turn Azure OpenAI Conversations

mythri mythri 20 Reputation points
2025-10-18T17:17:15.0233333+00:00

I’m building a chatbot using Azure OpenAI and I’ve noticed that in multi-turn conversations, the model often loses context after 3–4 exchanges. Even with system prompts reminding it of previous answers, it sometimes repeats itself or gives irrelevant responses.

Are there recommended strategies in Azure OpenAI to maintain context better over longer conversations? Should I be managing conversation history manually, or are there built-in features to help with multi-turn context?

Azure AI Bot Service
Azure AI Bot Service
An Azure service that provides an integrated environment for bot development.
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  1. Azar 30,735 Reputation points MVP Volunteer Moderator
    2025-10-18T17:52:45.74+00:00

    Hi there

    In Azure OpenAI, context retention is limited by the model’s maximum token window, so after several exchanges, older messages get truncated. The best practice is to manage conversation history manually: store prior messages and selectively include relevant parts in each prompt rather than the full chat. You can also use summarization to compress earlier context into a shorter form. Additionally, for multi-turn bots, consider embedding key conversation points in a vector store and retrieving them as context for the model — this helps maintain continuity without hitting token limits. There isn’t a built-in “infinite memory,” so careful prompt engineering and context management are essential for longer conversations.

    If this helps kindly accept the answr

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