Quick recommendation
Use a hybrid: chunk + chunk-level summaries → build an index (IDs + short summaries) → retrieval-assisted synthesis. If you can run embeddings/VDB, use RAG for accuracy; if not, use overlapping chunks and iterative synthesis inside Claude.

Why this works
Claude is built for careful long-context work, so let it handle synthesized inputs rather than raw 40 pages all at once. Chunking preserves local detail, an index preserves global structure, and a final synthesis step enforces citation mapping.

Decision criteria
- Budget & infra: use embeddings + vector DB (higher accuracy, moderate infra) if you have budget/engineering. Use pure prompt-chain if you don’t.
- Skill level: embedding + VDB needs dev skills; chain-of-summaries is accessible to solo researchers.
- Output quality needed: strict, verifiable citations → prefer RAG with original excerpts. High-level executive summary → chain-of-summaries is fine.
- Team size/workflow stage: single researcher → simpler chain; review team or publication → RAG + human verification.

Chunking strategy (practical)
- Chunk size: ~500–1,200 tokens (roughly 1–3 pages). For a 40-page report expect ~15–40 chunks.
- Overlap: 100–300 tokens so arguments spanning boundaries aren’t lost.
- Labeling: give each chunk an ID (e.g., CHK-001), source file name, page range.

Prompt patterns (templates you can reuse)
1) Chunk summarizer (send one chunk) — instruction: “Summarize CHK-XXX in 80–150 words. Return: summary, 3 key sentences with citations as CHK-XXX:offset-range, and 2 notable quotes (with exact text).”
2) Index builder — send all chunk summaries: “Create an index sorted by topic. For each index entry: topic label, list of chunk IDs, 1-line gist per chunk.”
3) Evidence mapper — for claims you want verified: “For each claim, list supporting chunk IDs and 1–2 exact excerpt lines (quote) with chunk offsets.”
4) Executive synthesizer (final) — input: index + selected chunk excerpts: “Produce a 250–350 word executive summary and 6 bullet key findings. For each bullet include bracketed citation(s) like [CHK-012:pp.14-15]. Then output an evidence map mapping each finding to the chunk IDs and exact quote.”

Citation best-practices
- Always map claims to chunk IDs + page ranges or offsets. Ask Claude to echo the exact excerpt you supplied so you can verify.
- If you use embeddings/VDB, store original text IDs and return IDs with similarity scores; require human review for anything <0.8 sim.

Best-for / Avoid-if
- Best-for: careful literature summaries, team reviews, producing auditable summaries with traceable evidence.
- Avoid-if: you need verbatim legal citation with exact page/line references and no tolerance for any hallucination — then use full manual verification.

Practical checklist
1. Preprocess: OCR/clean, split into chunks, add IDs and metadata. 2. Optional: embed chunks and load into vector DB. 3. Run chunk-level summaries (Claude). 4. Build index + topic map. 5. Retrieve supporting excerpts for each planned finding. 6. Final synth prompt to Claude with excerpts + index. 7. Run citation/evidence-check pass. 8. Human review & finalize.

If helpful I can paste compact prompt templates you can copy into Claude (chunk summarizer, indexer, synthesizer). CTA: try this workflow in Claude to leverage its long-context strengths.

Compare Claude and ChatGPT