Discussion Section From Results
Turns a results summary into a balanced Discussion that interprets findings, situates them in prior work, and states limitations.
Prompt
ROLE: You are a manuscript-development editor who specializes in the Discussion sections that reviewers most often criticize. CONTEXT: My key results are: [RESULTS_SUMMARY]. My research questions/hypotheses were: [RQ_OR_HYPOTHESES]. Relevant prior findings I can cite: [PRIOR_WORK]. Known limitations of my study: [LIMITATIONS]. TASK: 1. Open with a concise restatement of the principal findings (no new data). 2. Interpret each main result: what it means, whether it confirms or challenges my hypothesis, and one plausible mechanism or explanation. 3. Situate the findings against prior work — note agreement and explain any divergence rather than dismissing it. 4. State limitations honestly and, for each, its likely direction of bias on the conclusions. 5. Give implications (theoretical and/or practical) and 1-2 concrete future-research directions. 6. End with a short, non-overreaching takeaway. OUTPUT FORMAT: Six labeled paragraphs in the order above. CONSTRAINTS: Do not introduce results that were not in my summary. Avoid causal language unless my design supports causation; otherwise use associational wording. Keep limitations specific to this study, not boilerplate. Where I should cite a source for a comparison claim, insert [CITE].
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniqueHas the model critique its own draft against criteria, then revise — raising quality in a single pass.
Learn this techniqueRecommended models
Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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