This is a deep-seated challenge in prompt engineering: getting the AI to move from summarizing common explanations to truly dissecting the logical and rhetorical structure of those explanations, especially when they pertain to the very query it's analyzing. It requires forcing the AI to identify and then attack the inferential leap made when an observation is linked to a causal explanation provided by a theory. I will make further, even more forceful revisions to the analyzeQueryDeeply prompt in services/geminiService.ts. The goal is to make the AI: 1. **Isolate the raw phenomenon** in observationText with extreme prejudice against any embedded explanatory language. 2. Frame any **causal claim** (especially one involving the query's central idea) strictly as part of the interpretationText – as a hypothesis or proposal made by that interpretation. 3. Use the counterArgumentsToInterpretation section to **aggressively dissect that specific causal claim**, pointing out assumptions, circularity, alternative causal pathways, and rhetorical fallacies. **Key Prompt Enhancements for analyzeQueryDeeply in services/geminiService.ts:** 1. **Ultra-Strict Neutrality for observationText (Reiteration with more force):** - "The observationText MUST be a description of a raw, measurable phenomenon. It must NOT contain any language that explains the phenomenon or attributes its cause, especially if that cause involves the subject of the user's query. It is the effect, not the proposed cause and effect. For example, if the query is 'Theory T (which posits entity E causing phenomenon P) is correct', the observation is 'P is observed', NOT 'P is observed and is caused by E as per Theory T'." 2. **Framing Causal Links as Claims within interpretationText:** - "When an interpretationText links an observationText to the user's query (or an alternative) by positing a causal relationship (e.g., 'The observed phenomenon P is explained by/is a consequence of/is caused by the mechanism/entity described in Query Q'), this causal assertion itself IS THE CORE OF THE INTERPRETATION'S CLAIM. State it as such (e.g., 'This interpretation proposes that phenomenon P is caused by...'). Do NOT present this causal link as a settled fact within the interpretation if it's the very link under examination." 3. **Aggressive Deconstruction in counterArgumentsToInterpretation (Reinforced):** - "This section is where you demonstrate rigorous logical and rhetorical analysis. If the interpretationText makes a causal claim (e.g., 'Phenomenon P is caused by Query_Subject Q'): - **Identify the Nature of the Claim:** Is this causal link directly observed, inferred, or stipulated by a model? - **Challenge Circularity:** Explicitly state if the interpretation is merely re-stating the query's central hypothesis as an explanation for an observation. (e.g., 'This interpretation argues that P is evidence for Q because Q itself is defined as the cause of P. This is circular if the causal link is not independently verifiable and is the point of contention.'). - **Unpack Multi-Part Claims:** If the interpretation bundles claims (e.g., 'Observation O reveals problem X, and only my proposed entity Y can solve X'), dissect each component: Is O truly X? Is Y truly the only solution? Does Y solve X without other issues? - **Identify Rhetorical Fallacies:** Is this argument 'begging the question'? Is it an 'argument from ignorance'? Is it a 'post hoc ergo propter hoc' fallacy? Is it a 'false dilemma' by presenting limited options? - **Demand Necessity vs. Consistency:** Does the observation necessitate this causal interpretation, or is it merely consistent with it, while other interpretations (perhaps with different assumptions or fewer assumptions) are also consistent? - **Explore Alternative Causal Chains:** 'What other plausible mechanisms or factors could lead to the observed phenomenon P, without invoking Query_Subject Q?'" 4. **Updated Negative Example in Prompt (Illustrating the specific flaw you pointed out):** - "**Query Example:** 'The hypothesis that dark matter provides the additional gravity needed to explain galaxy cluster dynamics is correct.'" - "**INCORRECT interpretationText (for an interpretation supporting the query):** 'The gravitational mass discrepancy in galaxy clusters is caused by the presence of large quantities of dark matter within the clusters, which contributes significantly to their total gravitational potential...'" - (Self-correction guidance for AI within the prompt's internal thinking process): This is wrong because it states the query's core causal claim ('dark matter causes the discrepancy') as if it's the interpretation's found truth, rather than the hypothesis the interpretation is putting forward for analysis. - "**CORRECT interpretationText:** 'This interpretation posits that the observed gravitational mass discrepancy in galaxy clusters can be accounted for by the presence of large quantities of non-baryonic dark matter. The hypothesis here is that this dark matter contributes the additional gravitational potential needed to match observations.'" - (Self-correction guidance): This is better because it frames the causal link ('dark matter accounts for...') as the proposal of the interpretation, which can then be rigorously attacked in counterArgumentsToInterpretation by asking, 'Okay, this interpretation proposes dark matter as the cause. Is this necessary? What are the assumptions? Is it circular if this is the very query we're testing?' This level of detailed instruction and self-correction examples within the prompt is crucial for guiding the AI towards the desired analytical depth.