Efficient Context Summarization: Keeping Long Sessions Accurate

Long-running sessions exceed context limits quickly.

Summarization preserves the signal while keeping token budgets under control.

Extractive summaries preserve exact language but can be verbose.

Abstractive summaries are compact but can introduce subtle errors.

Summarize in layers: per turn, per section, then overall.

This prevents loss of critical details during compression.

Measure factual retention and decision consistency across time.

User validation is key for workflows with high stakes.

Store summaries alongside raw logs for auditability.

Use retrieval to pull raw evidence when summaries are uncertain.

Use periodic spot checks against the original transcript. Summaries can drift over time and lose nuance if never validated.

Add explicit fields for decisions, action items, and unresolved questions. Structured summaries reduce ambiguity and improve downstream retrieval.

Keep a small gold set of conversations to compare summary accuracy across model updates.

Store user feedback on summaries so you can prioritize fixes for the most painful errors.

Review summary errors by category to decide whether to improve prompts or add retrieval support.

Track summary length and compression ratios to ensure consistency across sessions.

Add a freshness indicator so users know when a summary was last validated.

Maintain a manual override to re-summarize with higher fidelity when accuracy is critical.

Provide users with a quick way to report missing details in summaries.

Include a recall checklist for critical facts so summaries never omit essential items.

Offer summary diff views so reviewers can see what changed between versions.

Maintain a fallback to the full transcript when confidence is low.

Create a standard template so summaries are consistent across teams and projects.

Flag summaries that exceed token budgets so they can be trimmed without losing key facts.

Does summarization reduce accuracy? It can if unchecked, which is why evaluation matters.

How often should I summarize? Whenever the context approaches model limits.

What is the safest approach? Keep both summaries and raw logs.