A conversational assistant can remember earlier messages yet still give irrelevant answers when it fails to update its understanding. Context management keeps track of prior interactions, interprets new cues, and revises the assistant’s internal state as the conversation changes. section 9.3.3
Effective management combines context-aware algorithms, natural language processing, user history, and preferences. It connects details spread across exchanges so responses remain coherent, accurate, and relevant rather than treating every message as isolated. section 9.3.3 section 9.4
Changing travel preferences
A user first requests budget travel options, then mentions wanting luxury accommodations. A context-aware assistant updates the active preference and changes its recommendations instead of continuing with budget-only results. section 9.3.3
Reliable context management requires:
- Tracking and updating context throughout the interaction. section 9.3.3
- Recognizing shifts in user intent and preferences in real time. section 9.3.3
- Combining retrieval-augmented generation with contextual understanding, task execution, and customer-data alignment rather than relying on retrieval alone. section 9.3.3 section 9.4
- Monitoring performance, incorporating feedback, and optimizing adaptive flows as conversations evolve. section 9.4
