How to set up ChatGPT for literature reviews
I need a repeatable prompt + chunking strategy to summarize and synthesize dozens of papers into a structured review. Looking for best practices on prompts, context windows, and citation extraction.
Best tools for this use case
Based on the workflow in this discussion, these tools are useful starting points to review.
ChatGPT
Best all-round AI assistant for broad knowledge work and workflow acceleration.
Claude
Excellent for careful reasoning, long-form thinking and structured analysis.
Gemini
Strong AI assistant for users already working inside Google's ecosystem.
Answers
Approved replies, operator insight, and tactical follow-up from the community.
Quick, repeatable workflow:
- Chunking: split papers into ~1,500–2,000 token chunks (~800–1,200 words) with ~200-token overlap; label chunks with ID+source.
- Chunk prompt (use per chunk): “Extract: purpose; methods; 3 key findings (bullets); limitations; exact citation (authors, year, DOI/URL). Output JSON: {id, purpose, methods, findings[], limitations[], citation{authors,year,doi,url}}.”
- Synthesis: merge chunk JSONs by theme, deduplicate, produce 200–400 word theme summaries and a consolidated citation CSV.
- Context windows: if model >32k tokens, feed merged chunks; otherwise do iterative merging (group→synthesize→merge).
- Citation QA: run a DOI/URL validation step (CrossRef/Unpaywall API) on extracted citations.
For long-context merging, consider Claude for careful, large-context synthesis.