FeedSummary.com Summary prompts
AI FEED SUMMARY ✦ RSS SUMMARY ✦ ATOM SUMMARY ✦ JSON FEED ✦ NEWS API ✦ ARTICLE SUMMARY ✦ PDF TO AI ✦ MARKDOWN ✦ WEBSITE SCRAPING ✦ AI NEWS AGENTS ✦

AI feed summary blueprints

Turn feeds into signal.

Practical workflows for summarizing RSS, Atom, JSON Feed, news APIs, articles, PDFs, Markdown, and selected websites—with source identity, evidence, and editorial control intact.

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Static, implementation-focused guidesNo invented performance claimsBuilt for traceability
12 source typesevidence attached Illustrated feed summary dashboard showing sources, a synthesized signal, evidence IDs, novelty, coverage, and verification status
Summarize from R RSSA AtomJ JSON FeedN News APIs↗ ArticlesP PDFs# MarkdownW Websites

Source coverage

One pipeline. Many source types.

A feed summary system works best when every source becomes a traceable record before a model writes anything.

AI feed summaries

Design item, cluster, and briefing summaries with explicit fields for evidence, uncertainty, and source identity.

Build the workflow

RSS feed summaries

Parse real-world feeds, select the best content, preserve dates and GUIDs, and deduplicate before synthesis.

Read the RSS guide

Atom + JSON Feed

Normalize typed content, stable IDs, authors, links, and attachments into one format-neutral record.

Compare formats

News API briefings

Turn query results into clustered, novelty-ranked briefings instead of a repetitive stream of headlines.

Map the pipeline

Documents + Markdown

Keep page labels, headings, lists, tables, and block IDs visible as long documents become compact summaries.

Process documents

Web pages + agents

Use bounded scraping, change detection, verified tool calls, and human approval gates for automated monitoring.

Design an agent

The signal path

Source in. Evidence out.

Every stage has one job and a visible handoff. That separation makes errors easier to find and summaries easier to trust.

See every workflow

Collect

Retrieve approved feeds, API results, documents, or pages while preserving the original payload and retrieval metadata.

Normalize

Map IDs, links, dates, authors, headings, paragraphs, tables, and attachments into a shared block model.

Cluster

Remove exact duplicates, group related coverage, and retain differences instead of counting copies as consensus.

Summarize

Use a versioned output contract for item records, topic synthesis, alerts, or executive briefings.

Verify

Check schema, evidence IDs, dates, quantities, entities, coverage, and review thresholds before publishing.

Diagram showing RSS, Atom, JSON, Markdown and PDF converging into one normalized source record

Format-neutral by design

Normalize without flattening.

Different source formats carry different strengths. Keep their original fields, then map them into a shared record with stable identity, dates, content blocks, and evidence locations.

  • Preserve raw fields and format-specific extensions.
  • Keep publication and modification time separate.
  • Retain headings, lists, tables, code, captions, and pages.
  • Label excerpt-only and partial extraction clearly.
Explore feed formats

A visible output contract

From noisy item to useful brief.

A summary can be concise without becoming opaque. Keep the input record, structured output, and evidence IDs connected.

Source record

Feed item FS-204

Title: Product update adds export controls

Published: 2026-10-14 09:00 UTC

Coverage: Full article, 18 evidence blocks

Cluster: 3 related sources, 1 syndicated copy

Structured summary

Export controls are now available to workspace administrators.

  • The change applies to organization-level exports.
  • Existing user permissions remain unchanged.
  • The publisher states rollout begins October 21.
  • Open question: whether legacy exports inherit the new policy.

Evidence: b04 · b07 · b11 · source FS-204

API-ready records

Design the contract before the call.

Stable fields make summaries renderable, searchable, testable, and portable across models. The API layer should expose extraction coverage, warnings, versions, and evidence—not only generated prose.

View API architecture
summary.json
{
  "source_id": "rss-104",
  "summary": "A neutral, evidence-linked briefing...",
  "key_points": ["..."],
  "evidence_ids": ["b04", "b11"],
  "coverage": "full_text",
  "status": "verified"
}

Feed Signals

Latest from Feed Signals

Long-form implementation guides for feed formats, document processing, prompts, scraping, and agentic news workflows.

Browse all 10 guides

Frequently asked

Feed summary FAQ

Clear answers to common architecture and implementation questions.

What is an AI feed summary?

An AI feed summary is a concise, structured representation of one or more feed items. A dependable workflow retains source IDs, dates, links, content coverage, and evidence locations so the summary can be checked.

Can RSS, Atom, and JSON Feed use the same pipeline?

Yes. Each format needs its own parser, but all can map into a shared internal record. The mapping should preserve raw fields and format-specific semantics instead of flattening everything into title and text.

How are PDF summaries different from article summaries?

PDF workflows must preserve page labels, reading order, tables, scans, and extraction method. Long documents usually need section-level records before a document-level synthesis.

Should a news agent publish automatically?

Only within explicit policy. Retrieval, ranking, summarization, verification, human review, and publishing should be separate stages, with approval rules based on topic and destination risk.

Does FeedSummary.com offer a live summarization service?

This static site is an implementation-focused reference and design blueprint. It does not collect user data or provide an interactive summarization form.

Build summaries that can show their work.

Start with source records, choose a format-neutral schema, and use a prompt contract that keeps evidence and uncertainty visible.

Open the prompt library