diff --git a/src/services/wiki_page_writer.py b/src/services/wiki_page_writer.py
index 1307d34..e402a4c 100644
--- a/src/services/wiki_page_writer.py
+++ b/src/services/wiki_page_writer.py
@@ -131,8 +131,8 @@ class WikiPageWriter:
             conflicts=conflicts
         )
 
-        # Reconstruct with LLM
-        reconstructed = await self._call_llm(prompt)
+        # Reconstruct with LLM (lower temperature for precise merging)
+        reconstructed = await self._call_llm(prompt, temperature=0.2)
 
         # Ensure standard sections are present
         reconstructed = self._ensure_standard_sections(
@@ -155,7 +155,7 @@ class WikiPageWriter:
         Returns:
             List of conflicts with: {fact_a, fact_b, confidence, context}
         """
-        prompt = f"""Analyze these two pieces of content for factual conflicts.
+        prompt = f"""Analyze these contents for direct factual conflicts.
 
 EXISTING CONTENT:
 {existing_content[:2000]}
@@ -163,25 +163,26 @@ EXISTING CONTENT:
 NEW INFORMATION:
 {new_information[:2000]}
 
-Identify any facts that contradict each other. For each conflict, provide:
-1. The fact from existing content
-2. The contradicting fact from new information
-3. Confidence level (low/medium/high)
-4. Context/explanation
+ANALYSIS STEPS:
+1. Identify specific factual claims in existing content (dates, numbers, names, states)
+2. Identify specific factual claims in new content
+3. Compare ONLY for direct contradictions (X says A, Y says not-A)
 
-Return ONLY valid JSON:
+RULES:
+- Do NOT flag differences in wording or phrasing as conflicts
+- Do NOT flag new/additional information as conflicts
+- Do NOT flag opinion differences as conflicts
+- ONLY flag direct factual contradictions
+- Return valid JSON only, no commentary
+
+Return format:
 {{
   "conflicts": [
-    {{
-      "existing_fact": "fact from old content",
-      "new_fact": "contradicting fact",
-      "confidence": "medium",
-      "context": "explanation of why these conflict"
-    }}
+    {{"existing_fact": "...", "new_fact": "...", "confidence": "low/medium/high", "context": "..."}}
   ]
 }}
 
-If no conflicts, return: {{"conflicts": []}}
+If no conflicts: {{"conflicts": []}}
 
 JSON:"""
 
@@ -189,7 +190,8 @@ JSON:"""
             response = await self.ollama.generate_text(
                 prompt=prompt,
                 model=self.model,
-                stream=False
+                stream=False,
+                temperature=0.0  # Deterministic for consistent conflict detection
             )
 
             # Extract JSON
@@ -348,6 +350,13 @@ FORMATTING RULES:
 - Keep sections focused and scannable
 - Adapt structure to content - not all sections apply to all topics
 
+CRITICAL CONSTRAINTS:
+- Do NOT invent facts not present in the source information above
+- Do NOT add speculative information or assumptions
+- Do NOT fill sections with placeholder text or generic statements
+- If information for a section is not available, OMIT the section entirely
+- Base ALL content strictly on provided source information
+
 Generate ONLY the markdown content (do not include Sources, Knowledge Graph, or Mind Map sections - those are added automatically).
 
 MARKDOWN:"""
@@ -403,6 +412,13 @@ FORMATTING RULES:
 - Bold important terms
 - Add subsections (###) where it improves clarity
 
+CRITICAL CONSTRAINTS:
+- Do NOT rephrase facts in ways that change their meaning
+- Do NOT remove ANY information unless explicitly superseded by newer facts
+- Do NOT add information not present in existing content or new information
+- Preserve exact quotes, dates, numbers, and names verbatim
+- Do NOT fill gaps with assumptions or general knowledge
+
 OUTPUT INSTRUCTIONS:
 - Return complete page content (do not include Sources, Knowledge Graph, Mind Map - those are added automatically)
 - Include updated "Changes & Updates" section noting what was changed today
@@ -410,13 +426,21 @@ OUTPUT INSTRUCTIONS:
 
 RECONSTRUCTED MARKDOWN:"""
 
-    async def _call_llm(self, prompt: str) -> str:
-        """Call LLM with prompt and return response."""
+    async def _call_llm(self, prompt: str, temperature: float = 0.3) -> str:
+        """
+        Call LLM with prompt and return response.
+
+        Args:
+            prompt: The prompt text
+            temperature: Sampling temperature (0.0=deterministic, higher=creative)
+                        Default 0.3 for controlled but natural content generation
+        """
         try:
             response = await self.ollama.generate_text(
                 prompt=prompt,
                 model=self.model,
-                stream=False
+                stream=False,
+                temperature=temperature
             )
 
             if not response: