Large Language Models are inherently stateless. Creating conversational memory is not about finding a smarter model, but about engineering a robust backend infrastructure. The true intelligence of a multi-turn AI assistant resides in this system's ability to manage state, not the model itself.
To maintain long-term context without fatal latency, do not summarize history during a live request. Instead, use database streams (like DynamoDB Streams) to trigger an asynchronous background worker. This worker condenses older messages into a rolling summary, which is then fetched quickly during the live request.
A common mistake in NoSQL schema design for AI chat is partitioning by user, which causes 'hot partitions' and throttling at scale. The correct approach is to partition by conversation ID for the AI's 'hot path' and use a secondary index for the UI's 'cold path' (e.g., listing a user's chats).
A common anti-pattern is interleaving dynamic data like UI state or user permissions directly into the conversational history sent to an LLM. This 'poisons the semantic chain' and causes context loss. Resilient systems use strict schema separation, placing system telemetry in a dedicated configuration block within the prompt.