A researcher drowning in 2,000 PubMed references for a nano-fertilizer review built a 12-step ChatGPT prompt that parses, dedupes, and scores every paper before he reads a single abstract himself. The subject-specific bits sit in square brackets, so the same skeleton works for any literature review, not just plant science.
Redditor u/arunshanker posted the full thing on r/PromptEngineering, and it’s less a “prompt” than a research protocol wearing a prompt’s clothes. He started using ChatGPT to sort references, noticed the process kept working no matter the topic, then generalized it with the model’s own help. The result is a 12-step pipeline that runs one step at a time and waits for you to say “proceed” before moving on. That pacing matters more than it sounds. Anyone who has dumped a huge task into a single mega-prompt knows the model starts skipping steps around step six, quietly collapsing three instructions into one and hoping you don’t notice. Forcing a checkpoint after every step means you catch drift on paper twelve instead of paper twelve hundred.
Why this matters more than a normal prompt
Most people ask an LLM to “summarize these papers” and get a confident, unverifiable mess back. This prompt does the opposite. It forces the model to state its scope, its confidence, and its uncertainty at every stage, instead of quietly guessing when information is thin. That’s the real unlock here. A summary hides its reasoning. A scored, sourced, confidence-tagged record shows its work, which means you can spot-check ten entries instead of re-reading all two thousand abstracts to see if the model missed something.
How the workflow actually runs
Step 1 parses the raw reference file into structured records (author, year, DOI, abstract) and flags anything broken or duplicated, so a mangled citation from a sloppy export doesn’t silently vanish from the dataset. Step 2 hunts duplicates and near-duplicates without deleting the audit trail, which matters when the same study shows up under a preprint DOI and a journal DOI six months apart. Step 3 does a deliberately generous first pass, sorting every paper into P1 through P4 by how central it looks to the review. Generous is the operative word: the instruction tells the model to err toward inclusion here, because a false negative in an early pass is much harder to catch than a false positive that gets trimmed later. Step 4 gets stricter, splitting survivors into core studies, mechanism studies, safety studies, economics, and more. From there it builds a full working database: theme, evidence maturity, relevance score from 1 to 5, and a note on whether the model is guessing or actually knows.
The part worth stealing for any project: UNCERTAIN is a first-class answer. Steps 5 and 11 both build in a category for “not enough information to decide,” with instructions to say so instead of manufacturing a verdict. That single rule is what keeps 2,000 papers from turning into 2,000 confident hallucinations. It also changes how you spend your own reading time. Instead of skimming everything equally, you go straight to the UNCERTAIN pile first, since that’s where the model is telling you, in plain language, exactly where it needs a human.
Use Cases
- 📚 Systematic literature reviews in any scientific field, medicine, agriculture, engineering, social science
- Sorting a huge backlog of customer feedback or support tickets into buckets with a confidence flag on each one
- Screening legal documents or contracts for relevance before a human reads the full text
- Triaging job applications, grant proposals, or vendor RFPs against a fixed set of criteria before a committee spends time on them
Prompt of the Day
Classify each item as P1 (clearly relevant), P2 (possibly relevant), P3 (probably not, uncertain), or P4 (clearly not relevant). For every item give one reason, the words that drove the decision, and a confidence level of high, medium, or low. If the information available doesn’t support a confident decision, output UNCERTAIN and state exactly what needs to be checked. Do not guess to fill a gap. Run this one step at a time and stop after each step for my review.
Swap “item” for whatever you’re sorting: resumes, tickets, contract clauses. The logic transfers without touching a word of the underlying instructions.
Where does this workflow actually break down? Once you hit batch three or four, when the model starts drifting on its own earlier classifications.
Frequently Asked Questions
Q: How do I stop keyword matching from becoming a shortcut?
Your keywords are guides, not strict rules. As one commenter who tried a similar approach for soil microbes found, this is actually the hardest part to get right. Don’t auto-exclude papers just because they use different terminology, skim abstracts even when the keywords aren’t a perfect match, since researchers often describe the same concepts differently.
Q: Does this workflow work for topics outside plant science?
Yes. The author built in square brackets around domain-specific parts so you can adapt it to any research area. A commenter already tested something similar on a soil microbes review and it worked well, confirming the core process travels across different fields.
Q: How well does this actually filter out irrelevant papers?
That depends on how thoughtfully you set up your inclusion/exclusion criteria upfront. The real payoff comes from being crystal clear about what counts as background material versus papers that are genuinely off-topic, get that right and you’ll save huge amounts of time screening.
Literature classification work flow
by u/arunshanker in PromptEngineering