refactor: improve wiki page writer prompts

Apply llm-findings.md recommendations:

Conflict detection (temp 0.0):
- Add explicit analysis steps (CoT)
- Strict rules: only flag direct contradictions
- Negative constraints for false positives

Page creation (temp 0.3):
- Add CRITICAL CONSTRAINTS section
- "Do NOT invent facts not in source"
- "Do NOT fill sections with placeholders"
- Omit sections if information unavailable

Page reconstruction (temp 0.2):
- Add preservation constraints
- "Do NOT rephrase facts changing meaning"
- "Preserve exact quotes, dates, numbers verbatim"

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
2025-12-23 17:21:58 +01:00
co-authored by Claude Opus 4.5
parent 8e003bb9e8
commit edfe11f0fb
+44 -20
View File
@@ -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: