The AI session summary missed important coding details
Summaries are useful when they are accurate. But long sessions often compress away the exact details that make continuation safe.
Why this hurts.
A summary can miss files changed, failed attempts, constraints, or what not to touch. It can sound confident while being incomplete.
How ShardStitch solves it.
Treat the summary as a lead
Treat summaries as one signal, not the ground truth.
Compare it with repository state
Rebuild from disk, git, files, notes, and available local state.
Restore omitted verification details
Restore the exact commands and outcomes omitted from the summary; distinguish passed, failed, and unrun.
Label what cannot be reconstructed
State which intentions or results cannot be recovered from disk or notes, and keep them as open questions.
What the next AI receives.
- Verified state
- Summary only when useful
- Missing-info warnings
- Next action
What stays out.
- Unsupported summary claims
- Old model self-reporting
- Vague progress descriptions
- Confident but unverified statements
Audit a summary before trusting it
FAQ.
Can I trust an AI-generated session summary?
Treat it as an aid, not an authority. Verify its claims against current files and recorded outputs, especially completion, test status, and rationale.
Can a new packet restore details the summary omitted?
Only when those details remain in project files, Git, notes, logs, or another available source. A packet cannot reconstruct missing conversation-only context.