Your AI Session Summary Fails the Next Chat: Build a Handoff With Decision Locks
You spend three evenings with an AI on a billing pipeline. On the fourth, you open a fresh chat, paste a summary the model wrote the night before, and ask it to continue. Within five messages it proposes Prisma. You rejected Prisma on day one, for a reason that took twenty minutes to establish. The summary was accurate. It was also missing the one thing the new session needed: what is already…
A single summary of an AI session is insufficient to continue the conversation effectively. Summaries compress information, often omitting crucial details like rejected options, soft constraints, and open questions. This leads to misunderstandings when the AI begins a new session without the full context.
The key to reliable AI handoffs is structuring the summary into six specific sections. These sections cover completed work, current progress, prioritized next steps, confirmed decisions and constraints, unresolved questions, and must-not-lose items. By explicitly listing what is closed and what is open, the handoff provides a clear boundary for the next session.
Decision locks are a critical component of the handoff that are often missing. These are specific decisions made during the previous session, along with the reasons behind them. Including these locks prevents the AI from re-debating settled matters and allows it to build upon the established foundation. Each decision lock should state the choice and the rationale, rather than just saying "rejected X".
The prompt provides a template for creating a handoff document, including a bootstrap block that guides the next session. This block instructs the AI to confirm understanding of the confirmed decisions, then proceed with the prioritized next steps. By confirming the state in concise bullet points, the next session can verify the handoff's accuracy before proceeding.
Different types of projects require different types of handoffs. A coding handoff might capture file paths, schema choices, and bug traces, while a writing handoff would focus on voice rules, outlines, and guardrails. The depth of the handoff can be adjusted using the handoff_depth variable, allowing for comprehensive state injection or a more focused summary.
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