Perconversation

Perconversation is a design concept used in software systems to scope data, state, or behavior to an individual conversation rather than to a user, a session, or the entire application. In practice, systems assign a unique conversation identifier and store context, memory, or settings under that key, keeping information separate from other conversations.

In chatbots, messaging platforms, and collaborative tools, perconversation enables the system to preserve the context of a single interaction. This allows it to remember details mentioned during that chat, track progress, and tailor responses to the specific thread without cross-contaminating other conversations.

Implementation typically relies on a conversation_id to index stored state. Data may be kept in in-memory caches for speed or in persistent databases for durability. Designers must balance memory usage, retrieval latency, and retention policies, especially in high-traffic environments where many concurrent conversations occur.

Privacy and security considerations are important with perconversation data. Depending on policy, stored context may include personally identifiable information or sensitive content, requiring access controls, encryption, and defined data retention and deletion practices.

Related concepts include per-user, per-session, and per-channel scoping, as well as general context management and stateful versus stateless architecture. Understanding perconversation helps clarify how long-term memory, personalization, and context carryover are managed within multi-threaded or multi-user systems.