FeedSummary.com Summary prompts

AI + LLM summarization

AI Feed Summary

A practical architecture for turning high-volume feeds into concise, traceable briefings without losing source context, dates, or uncertainty.

AI feed summaryLLM summaryevidence IDsstructured outputverification

Summary architecture

Three layers of useful compression

Do not ask one prompt to perform every stage. Build from evidence-preserving item records toward higher-level synthesis.

Item summaries

Create a compact record for one article, feed entry, report, or page with facts, dates, entities, and evidence IDs.

Cluster summaries

Synthesize related coverage after duplicate and syndication relationships are visible.

Briefing summaries

Rank validated clusters for a defined audience, time window, topic scope, and decision.

Structured output

A dependable output contract

The output schema should make absence, uncertainty, and provenance visible.

Ask for fields, not vibes.

Define a neutral headline, short abstract, key points, dates, entities, content type, open questions, and evidence IDs. Set field limits and require null values when the source does not support an answer.

  • Separate reported facts from analysis.
  • Preserve source and item identifiers.
  • Label excerpt-only or partial coverage.
  • Validate every response before rendering.
briefing.schema
headline: string
abstract: string ≤ 90 words
key_points: array ≤ 5
event_date: date | null
evidence_ids: string[]
coverage: full | partial | excerpt
verification: pass | warning | fail

Verification

Quality is a pipeline property

A fluent model response cannot repair a missing source record or scrambled extraction.

Upstream controls

Stable IDs, canonical URLs, separate publication and modification dates, structured content blocks, duplicate relationships, and source coverage belong in the record before summarization.

When these fields are missing, the summary should expose the limitation instead of guessing.

Downstream controls

Schema validation, number and date checks, evidence lookups, review thresholds, versioned prompts, and regression tests turn generated output into an observable system.

Track corrections by failure stage so extraction problems are not misdiagnosed as prompt problems.

Feed Signals

Recommended reading

Continue with the long-form workflow and prompt guidance.