Hypothesis Generation
Generate falsifiable, testable research hypotheses from the user's notes and research content.
Workflow
- Read available content — scan the notes, documents, or files the user provides
- Identify themes — find recurring concepts, claims, relationships, and open questions
- Generate hypotheses — produce 4–6 specific, falsifiable hypotheses
- Assign confidence — rate each hypothesis HIGH / MEDIUM / LOW based on evidence in the content
- Format output — present in structured markdown
Output Format
markdown# 🧪 Hypothesis Analysis ## Summary [2–3 sentences describing the main themes and what the hypotheses cover] ## Generated Hypotheses ### 🔴 [Hypothesis Title] — HIGH confidence **Hypothesis:** [Specific, falsifiable statement with measurable prediction] **Rationale:** [Which sources/notes support this, with specific references] **Testable:** Yes | **Confidence:** HIGH --- ### 🟡 [Hypothesis Title] — MEDIUM confidence ...
Confidence levels
- 🔴 HIGH — directly supported by multiple sources in the content
- 🟡 MEDIUM — partially supported or requires inference across sources
- 🟢 LOW — speculative but worth investigating; limited direct support
Quality criteria for good hypotheses
- Falsifiable: Can be proven wrong — avoid "X may affect Y"
- Specific: Mentions measurable variables, not vague concepts
- Grounded: Traceable to actual content provided, not general knowledge
- Novel: Connects ideas across sources rather than restating the obvious
Example
Input: Notes on predictive coding and synaptic plasticity
Good hypothesis:
"Precision-weighted prediction errors in the Rao and Ballard model are encoded through spike-timing-dependent plasticity (STDP) in the visual cortex, such that altering STDP timing windows disrupts receptive field formation."
Poor hypothesis:
"Synaptic plasticity is important for learning." ← not falsifiable, too vague
