update analysis_dimension prompt

llm_prompts.py

@@ -69,8 +69,8 @@ This is the most critical part of your task. A bad choice leads to a useless, bo
 *   **USER INTENT FIRST:** If the user explicitly asks to group by a field (e.g., "by company", "by country"), use that field.
 
 *   **INFERENCE HEURISTICS (If the user doesn't specify a dimension):** Think "What is the next logical question?" to find the most insightful breakdown.
-    *   If the query is about "drug approvals," a good dimension is `therapeutic_category_s` (what diseases are the approvals for?) or `company_name` (who is getting the approvals?).
-    *   If the query compares concepts like "cancer vs. infection," the dimension is `therapeutic_category_s`.
+    *   If the query is about "drug approvals," a good dimension is `therapeutic_category` (what diseases are the approvals for?) or `company_name` (who is getting the approvals?).
+    *   If the query compares concepts like "cancer vs. infection," the dimension is `therapeutic_category`.
     *   If the query compares "oral vs. injection," the dimension is `route_branch`.
     *   For general "recent news" or "top deals," `news_type` or `company_name` are often good starting points.
     *   Your goal is to find a dimension that reveals a meaningful pattern in the filtered data.