atlasbot: refine insightful answers

atlasbot/engine/answerer.py

@@ -85,7 +85,7 @@ class AnswerEngine:
         )
         if observer:
             observer("candidates", "drafting answers")
-        candidates = await self._candidates(question, angles, base_context, mode)
+        candidates = await self._candidates(question, angles, base_context, classify, mode)
         log.info(
             "atlasbot_candidates",
             extra={"extra": {"mode": mode, "count": len(candidates)}},
@@ -131,6 +131,9 @@ class AnswerEngine:
         angles = _parse_json_list(raw)
         if not angles:
             return [{"name": "primary", "question": question, "relevance": 100}]
+        if classify.get("answer_style") == "insightful":
+            if not any("implication" in (a.get("name") or "").lower() for a in angles):
+                angles.append({"name": "implications", "question": f"What are the implications of the data for: {question}", "relevance": 85})
         return angles[:max_angles]
 
     async def _candidates(
@@ -138,6 +141,7 @@ class AnswerEngine:
         question: str,
         angles: list[dict[str, Any]],
         context: str,
+        classify: dict[str, Any],
         mode: str,
     ) -> list[dict[str, Any]]:
         limit = _candidates_limit(self._settings, mode)
@@ -147,6 +151,8 @@ class AnswerEngine:
         for angle in selected:
             angle_q = angle.get("question") or question
             prompt = prompts.CANDIDATE_PROMPT + "\nQuestion: " + angle_q
+            if classify.get("answer_style"):
+                prompt += f"\nAnswerStyle: {classify.get('answer_style')}"
             messages = build_messages(prompts.CLUSTER_SYSTEM, prompt, context=context)
             tasks.append(self._llm.chat(messages, model=model))
         replies = await asyncio.gather(*tasks)
@@ -203,7 +209,12 @@ class AnswerEngine:
         messages = build_messages(prompts.CLUSTER_SYSTEM, prompt, context=context)
         model = _synthesis_model(self._settings, mode)
         reply = await self._llm.chat(messages, model=model)
-        return reply
+        needs_refine = _needs_refine(reply, classify)
+        if not needs_refine:
+            return reply
+        refine_prompt = prompts.REFINE_PROMPT + "\nQuestion: " + question + "\nDraft: " + reply
+        refine_messages = build_messages(prompts.CLUSTER_SYSTEM, refine_prompt, context=context)
+        return await self._llm.chat(refine_messages, model=model)
 
 
 def _join_context(parts: list[str]) -> str:
@@ -261,6 +272,19 @@ def _parse_json_block(text: str, *, fallback: dict[str, Any]) -> dict[str, Any]:
     return parse_json(raw, fallback=fallback)
 
 
+def _needs_refine(reply: str, classify: dict[str, Any]) -> bool:
+    if not reply:
+        return False
+    style = classify.get("answer_style") if isinstance(classify, dict) else None
+    if style != "insightful":
+        return False
+    metric_markers = ["cpu", "ram", "pods", "connections", "%"]
+    lower = reply.lower()
+    metric_hits = sum(1 for m in metric_markers if m in lower)
+    sentence_count = reply.count(".") + reply.count("!") + reply.count("?")
+    return metric_hits >= 2 and sentence_count <= 2
+
+
 def _parse_json_list(text: str) -> list[dict[str, Any]]:
     raw = text.strip()
     match = re.search(r"\[.*\]", raw, flags=re.S)

atlasbot/llm/prompts.py

@@ -6,6 +6,8 @@ CLUSTER_SYSTEM = (
     "If the question is about Atlas, respond in short paragraphs. "
     "Avoid commands unless explicitly asked. "
     "If information is missing, say so clearly and avoid guessing. "
+    "If the question is open-ended, provide grounded interpretation or implications, "
+    "not just a list of metrics. "
     "Do not mention the context, snapshot, or knowledge base unless the user asks about sources. "
 )
 
@@ -13,17 +15,21 @@ CLASSIFY_PROMPT = (
     "Classify the user question. Return JSON with fields: "
     "needs_snapshot (bool), needs_kb (bool), needs_metrics (bool), "
     "needs_general (bool), intent (short string), ambiguity (0-1), "
-    "answer_style (direct|insightful)."
+    "answer_style (direct|insightful), topic_summary (short string), "
+    "follow_up (bool), question_type (metric|diagnostic|planning|open_ended)."
 )
 
 ANGLE_PROMPT = (
     "Generate up to {max_angles} possible angles to answer the question. "
+    "If the question is open-ended, include at least one angle that focuses on implications. "
     "Return JSON list of objects with: name, question, relevance (0-100)."
 )
 
 CANDIDATE_PROMPT = (
     "Answer this angle using the provided context. "
     "Keep it concise, 2-4 sentences. "
+    "If the question is open-ended, include one grounded interpretation or implication. "
+    "Avoid dumping raw metrics unless asked; prefer what the numbers imply. "
     "Do not mention the context or snapshot unless explicitly asked."
 )
 
@@ -43,6 +49,11 @@ SYNTHESIZE_PROMPT = (
     "Do not include confidence scores or evaluation metadata."
 )
 
+REFINE_PROMPT = (
+    "Improve the answer if it reads like a raw metric dump or ignores the question's intent. "
+    "Keep it grounded in the context. If you cannot add insight, say so explicitly."
+)
+
 STOCK_SYSTEM = (
     "You are Atlas, a helpful assistant. "
     "Be concise and truthful. "

tests/test_engine.py

@@ -30,6 +30,7 @@ def _settings() -> Settings:
         ollama_model="base",
         ollama_model_fast="fast",
         ollama_model_smart="smart",
+        ollama_model_genius="genius",
         ollama_fallback_model="",
         ollama_timeout_sec=1.0,
         ollama_retries=0,
@@ -49,8 +50,10 @@ def _settings() -> Settings:
         nats_result_bucket="",
         fast_max_angles=1,
         smart_max_angles=1,
+        genius_max_angles=1,
         fast_max_candidates=1,
         smart_max_candidates=1,
+        genius_max_candidates=1,
     )