Amey-Thakur Conversation design
A conversation is a state machine the user cannot see. The design work is making that state coherent across turns: what the system remembers, how it handles topic shifts, and how it recovers when it or the user gets lost.
Method
- Separate durable state from turn context. Track the conversation's structured state explicitly (current goal, collected slots, decisions made, user identity) rather than trusting the model to re-derive it from raw history every turn; inject that state as compact facts (see context-window-management's pin rule, agent-memory for cross-session persistence). History compaction then cannot lose the load-bearing facts.
- Handle topic shifts as first-class events. Users abandon, digress, and return: detect topic changes, preserve the interrupted task's state for resumption ("we were setting up your export, want to finish?"), and do not force linear flows on non-linear humans. Rigid scripts that ignore "actually, different question" are the signature of bots users escape from.
- Ground each turn in the right context. Retrieve per-turn what this turn needs (see rag-pipeline), carry forward references ("it", "that one") by resolving them against tracked state, and re-inject constraints the model drifts from over long conversations (tone, role, refusal rules: see llm-guardrails). Long chats erode instruction-following; periodic re-grounding is the fix.
- Design repair explicitly. Misunderstanding recovery (the user says "no, I meant X": update state, do not restart), clarification when genuinely ambiguous (ask, do not guess and barrel on), and graceful "I don't know / let me get a human" paths (see llm-guardrails' escalation). The measure of a conversation system is how well it handles being wrong, not how well it handles the happy path.
- Bound the conversation's power at each turn. Actions the conversation can trigger go through the same validation and authz as any request (see tool-use-design, authz-design): a user (or an injected instruction in retrieved content) must not talk the system into privileged actions it would refuse directly. The dialogue is untrusted input end to end.
- Evaluate multi-turn, not single-turn. Test whole conversations: state carried correctly across turns, corrections honored, topic switches survived, repair working (see llm-eval-design's multi-turn cases); mine real transcripts for the failure patterns (see llm-observability) and turn each into a scripted eval. Single-turn QA evals pass while the actual conversational experience fails.
Boundaries
- Not everything should be a conversation: a form is faster than a chat for structured input, and forcing dialogue onto transactional tasks adds turns users resent (see mobile-input-ux's field-cutting: same instinct).
- Long conversations hit context limits; the compaction and state-externalization discipline (see context-window-management) is the prerequisite, not an afterthought.
- Voice adds latency, barge-in, and ASR-error handling on top of these text concerns; the state and repair design transfers, the turn-taking mechanics do not.