Short answer
Use Claude as your primary model for long-chain, provenance-sensitive literature reviews, but only when you pair it with a robust retrieval pipeline and human verification. ChatGPT is a reasonable fallback if you need broader plugin/ecosystem support.

Recommendation
- Primary: Claude — because it’s built for careful analysis and long-context work, it handles longer conversations and structured outputs more reliably for traceable citations. Use it for extraction, synthesis, and ongoing source-mapping.
- Fallback: ChatGPT — if you need plugin integrations, larger user community prompts/templates, or lower cost options; expect slightly more guardrails to avoid hallucination.

Why Claude works here (short rationale)
Claude’s strengths for your use case are: longer context retention across prompt chains, a bias toward cautious/analytic responses, and good ability to output structured provenance when prompted. Those make it easier to keep a running source map across many turns. But no model eliminates the need for strict pipelines and human checks.

Decision criteria (pick what matters most for your team)
- Citation fidelity: If you need verbatim quotes, page numbers, or DOIs, require rigorous retrieval and a verification pass.
- Context window: Long-context models help maintain conversation-level provenance without constantly re-supplying full documents.
- Auditability: You need machine-readable provenance (CSV/JSON) for reproducibility and auditing.
- Cost & throughput: Large-context inference is more expensive—budget matters for large corpora.
- Skill level: Teams with engineers can build RAG + vector DB workflows; non-engineering teams should rely on careful prompting + manual checks.

Practical checklist to implement a safe Claude-based workflow
1) Ingest reliably: store each PDF/article as an atomic object with a stable source_id, DOI, and metadata (title, authors, year, pages, URL).
2) Chunk & index: split texts into logical chunks (paragraphs/section-level) and index into a vector DB with source_id + chunk_offset.
3) Retrieval layer (RAG): always retrieve candidate chunks and attach source_id + chunk_offset to the prompt. Don’t let the model “remember” provenance—supply it.
4) Structured extraction prompt: ask Claude to output a machine-readable table for each claim: claim_text | exact_quote | page/para | source_id | DOI/URL | confidence (0–1) | notes.
5) Provenance tracking across turns: append latest provenance table to the chat state or store externally and reference by id in prompts (e.g., “see provenance_table#17”).
6) Verification pass: run a second pass that re-locates each quote in the original PDF (automatic matcher + human spot check). Flag mismatches.
7) Human-in-the-loop finalization: editors verify all quotes/DOIs before publication and freeze the provenance table as the canonical record.
8) Export & archive: export the provenance table as CSV/JSON and link it into your review management system.

Best-for / Avoid-if
- Best for: teams doing multi-stage systematic reviews who need structured provenance, have engineering support (or a low-friction RAG tool), and prioritize auditability.
- Avoid if: you can’t afford the extra compute/cost or have zero capacity for verification—models alone won’t guarantee citation fidelity.

Final note
Claude gives you better long-chain handling out of the box, but success depends more on your RAG pipeline, chunking/indexing strategy, and a verification step than on the model alone. If you want a quick starting prompt template or an example provenance table schema, say so and I’ll produce one tailored to your workflow.

Compare Claude and ChatGPT