Recommendation:
Use a retrieval-augmented, chunked pipeline with a canonical source map and iterative prompt-chaining. Ingest every source as small, overlapping chunks, store embeddings in a vector DB, and always return model outputs with explicit source anchors (DOC_ID:chunk#). Keep a compact project-level “canonical summary” that you update after each major revision so the model can reason across the whole 10k-word draft without re-ingesting everything.

Why this works (short):
- Chunking + embeddings = precise retrieval of evidence.
- Canonical summaries = condensed context you can fit in the model window.
- Prompt-chaining = repeatable ops (review, revise, cite) so changes stay linked to sources.

Decision criteria (pick based on budget, team, and quality needs):
- Budget: vector DB + embedding costs increase with scale. If budget is tight, store only top-N chunks per doc or use sparse retrieval.
- Skill level: engineers can automate ingestion/embedding; writers can run manual chunk+meta tagging with a spreadsheet.
- Team size/workflow stage: small solo authors can keep a single canonical summary and manual anchors; large teams should enforce strict source-id rules and automated pipeline.
- Output quality: high-stakes whitepapers need source-anchor policy (quote vs paraphrase) and a final human fact-check pass.

Concrete orchestration & tooling tips (practical):
1) Ingesting + chunking
- Chunk size: 400–800 tokens per chunk with 50–150 token overlap.
- For each chunk create: chunk_id (DOCID_chunkN), plain text, 1–2 sentence extractive summary, and 3–5 key claims (short bullets). Store those in your DB.
- Keep metadata: title, author, url, DOI, page-range.

2) Embedding & retrieval
- Create embeddings for chunk text and summary. Use cosine similarity to fetch top K (K=6–12) for any prompt. Always return chunk_ids with retrieved results.

3) Prompt-chaining patterns (templates to adapt)
- Ingest prompt: “Ingest DOCID X, CHUNK_ID Y. Produce: (a) 1-sentence summary, (b) 3 key claims, (c) canonical citation anchor format [DOCID:Y].”
- Review prompt: “Given SECTION A (current draft) and retrieved chunks [list chunk_ids + summaries], update SECTION A to improve accuracy/style while preserving claims and add inline anchors like [DOCID:chunk#] at sentence-level.”
- Revision/patch prompt: “Apply these edits (bulleted) to SECTION B and return a diff-style patch and list of impacted source anchors.”

4) Keep a canonical summary layer
- After every full draft pass, compress each section into a 100–200 word canonical summary. Store these as the “project memory” used as the main context for subsequent high-level edits.

5) Citation hygiene
- Always surface chunk_id alongside human-readable citation. At the end of the draft, run a final pass that replaces chunk_ids with full references (author, year, url, page) and flags any uncited claims for fact-check.

Best-for / Avoid-if
- Best-for: multi-source whitepapers that require traceability and repeated revision cycles (teams or solo with many sources).
- Avoid-if: single-author opinion pieces with few external sources — overhead may be too high.

Practical checklist (do this now)
- [ ] Assign canonical ID scheme for every source.
- [ ] Chunk all sources (400–800 tokens, 50–150 overlap).
- [ ] Generate and store summaries + key claims per chunk.
- [ ] Create embeddings, enable top-K retrieval.
- [ ] Add prompt templates: ingest, review, revise, final-citation.
- [ ] After each major revision, update section-level canonical summaries.
- [ ] Final pass: map chunk_ids → full citations and human fact-check.

Tool note: ChatGPT is a practical orchestration endpoint for writing and prompt-chaining; Claude can be used when you prefer extra-long-context analytic passes. Choose based on model window, cost, and your integration skills.

If you want, I can provide ready-to-use prompt templates (ingest, review, revision patch) and a JSON schema for your canonical source map.

Compare ChatGPT and Gemini