LLM sessions are ephemeral—when context fills or you close the window, everything learned vanishes. Session handoffs solve this by creating structured documents that bridge gaps, giving your AI assistant persistent memory that survives across sessions, tools, and team changes. This approach is for developers frustrated by re-explaining context every time they start a new session.

Built-in memory features like Claude's `/compact` or ChatGPT's Memory help but have limits: they're opaque, tool-locked, and don't survive session ends. Session handoffs are human-readable, AI-readable, searchable markdown files stored in your repo. They capture accomplishments, current state, lessons, next steps, and blockers, making institutional memory portable and version-controlled.

At session end, the AI drafts a handoff document saved to `docs/session_handoffs/`. The next session reads the most recent file, restoring context in seconds instead of 10-15 minutes. This workflow works with any AI assistant—Claude, Kiro, Cursor—and survives tool or team changes, turning stateless LLM sessions into cumulative knowledge.