You are deep into a conversation with Claude. You have spent the last hour shaping a proposal, debugging a stubborn issue, or building something iteratively. The model understands your tone, your constraints, and the decisions already made. Then something shifts. The responses become slightly generic. Issues you resolved earlier reappear. The thread feels thinner.
Nothing dramatic happened. You likely reached the edge of the context window. Understanding that constraint, and designing around it, is part of using AI competently.
What a Context Window Actually Is
Every AI model operates within a fixed context window. This is not memory in the human sense. The model does not remember you across separate conversations unless a system is explicitly built to do so. Within a single session, however, it can only “see” a finite amount of text at once.
That limit is measured in tokens, not words. Long documents, transcripts, and code blocks consume tokens quickly. Once the total conversation approaches the model’s maximum context size, earlier content may be truncated or effectively de-emphasized. At that point, the model cannot reliably reference everything that was previously discussed.
The degradation is often subtle. The model does not announce that it has lost context. Instead, it begins to respond in ways that are slightly misaligned. Settled issues resurface. Tone drifts. Answers become safer and more generic.
This issue is more acute with models that have smaller context windows. Some models top out in the 8k to 32k token range, where long technical discussions or iterative creative work can hit the wall quickly. Newer systems have significantly larger windows, sometimes in the 100k+ token range, so the problem arises much later, typically in marathon sessions or when processing very large documents.
But no context window is infinite.
Knowing When to Start a New Conversation
The most practical way to manage this issue is by learning to recognize the inflection point before performance degrades. Responses may also slow down noticeably (higher ‘latency’) as the context fills up, but by the time lagging becomes obvious, you’ve likely already seen substantial quality degradation.
You are usually at that point when:
- Responses feel generic or inconsistent with earlier decisions.
- You are shifting to a materially different subtask within the same project.
- The conversation is already long, and you are about to begin something high stakes.
- You want to reset assumptions and reframe the problem.
Opening a new conversation is not a failure. It is often the more deliberate choice. The key is preserving what matters.
The Handoff Document
A handoff document is a structured summary that allows you to continue the work in a new session without losing momentum. If a colleague joined your project today, you would not forward every message thread. You would provide a focused brief. The same principle applies here. A strong handoff document typically includes:
- The goal. A concise statement of what you are trying to accomplish.
- Key decisions already made. Issues that are settled and should not be reopened.
- Explicit “do not revisit” items. For example: “We have definitively chosen X over Y for reasons A and B. Do not revisit this decision.”
- Constraints and context. Audience, tone, technical requirements, exclusions, preferences.
- Current status. Where you left off.
- Immediate next step. The specific task you want the new session to perform.
That “do not reopen” section is particularly important. Models are naturally inclined to explore alternatives unless firmly instructed otherwise. If something is decided, say so clearly. This handoff document does not need to be long. Precision matters more than volume. A tightly structured paragraph or short bulleted list is often sufficient.
Let the Model Draft the Handoff, Then Review It
One efficient approach is to ask the model to generate the summary before you close the conversation. For example:
“Write a context summary I can paste into a new conversation. Include the project goal, decisions already made, constraints, current status, and the next step. Explicitly flag any decisions that are final and should not be revisited.”
Or:
“Create a handoff document with sections for Project Goal, Decisions Made, Do Not Reopen, Open Questions, and Next Steps. Keep it under 200 words.”
The model can synthesize what remains within the active context window. However, you should not treat the output as authoritative without review. Confirm that tentative ideas have not been reframed as firm conclusions and that important nuance has not been compressed away. If the conversation drifted or contained errors, it is often better to manually restate your objective rather than rely entirely on an automated summary.
Managing Multi-Session Projects Deliberately
For projects that will span multiple sessions, design for that reality from the outset.
Front-load your context. Begin the first conversation with a structured brief. That framing becomes reusable.
Maintain a living project summary outside the chat interface. This can be a document, a note, or a shared workspace. Update it as decisions are made. When you start a new session, paste the current version. Over time, it becomes a stable source of truth.
Human teams use project briefs, wikis, and shared documents for a reason. Treating the AI as a collaborator who requires a clear brief, rather than as an omniscient partner, leads to better outcomes.
Consider treating topic shifts as natural breakpoints. Moving from strategy to drafting, or from analysis to implementation, is often a clean place to open a new session with a concise handoff rather than dragging a long thread forward.
The Practical Takeaway
Long conversations degrade because the system has finite context. Once you understand that boundary, you can design around it. The handoff document is a simple mechanism that preserves continuity without forcing the model to carry unnecessary conversational weight.
Used consistently, a handoff document converts a structural limitation into a manageable workflow constraint. You close a session intentionally, carry forward what matters, and continue the work with clarity.