AI coding context window is full and the chat is slowing down
A full context window is a quality problem. The model is carrying too much state and too little certainty.
Why this hurts.
Compaction can be lossy, summaries can miss files, and long chats often preserve the wrong details.
How ShardStitch solves it.
Save the current working state
Record the active goal, stopping point, changed files, and next decision before replacing the full chat.
Preserve changed files and checks
Carry the working-tree diff, relevant paths, recent commits, and check results so the new session can inspect what changed.
Choose what crosses the boundary
Put task-critical files and constraints up front; retain deeper project context for follow-up rather than loading it all at once.
Verify the fresh session
Open a fresh session with the handoff, confirm the current branch and diff, then continue from the listed next action.
What the next AI receives.
- Hot task context
- Verified files
- Important decisions
- Next action
What stays out.
- Cold history
- Repeated explanations
- Unneeded file dumps
- Old attempts
Checkpoint before the context boundary
Context-window recovery checklist.
FAQ.
Is compacting the chat or running /compact enough?
Not by itself. Compaction may preserve conversational continuity, but it does not independently verify the current diff, files, command results, or test status. Treat the compacted summary as a lead, then check those facts against the repository and recorded outputs.
Does a full context window mean my code changes are lost?
Not necessarily. Saved files and Git state may remain on disk, but unsaved model output or decisions held only in chat may be gone. Inspect the actual project before deciding what is recoverable.
Can I continue in the same AI tool after the limit clears?
Often the project state can be handed to a fresh session in the same tool, subject to the tool limits and supported pickup format. The handoff does not reset the provider quota.