Evidence-based response prompt builder
Most prompts ask a model for an answer and accept whatever comes back at face value. The problem is that language models flatten the difference between a strong source and a weak one — a forum post and a systematic review get the same confident tone. This builder writes a prompt that forces the model to slow down: enumerate its evidence, label each item by strength, and let the weight of the evidence drive the conclusion rather than the other way around.
How it works
The generated prompt has three enforced phases. First the model lists every piece of evidence relevant to your question. Second, it tags each item against the evidence strength scale you pick — for example anecdotal, observational study, randomized trial, or meta-analysis. Third, it writes a conclusion that explicitly references the strongest available evidence and flags where the evidence is thin or conflicting. Citation and conclusion formats are configurable so the output drops cleanly into a report or a literature note.
Tips and notes
- Supply your own sources when you can. Paste documents below the prompt so the model grades real evidence rather than its training memory, which reduces fabricated citations.
- Watch for forced confidence. If the model still concludes strongly from weak evidence, add the line “if all evidence is anecdotal, say so and decline to make a strong claim.”
- Use the strength labels as a filter. When reviewing the output, read the meta-analysis and trial rows first — they carry the most weight and expose whether the conclusion is actually supported.