From 1d406f6a4e38962d1b9e9b4c46c391632197ca90 Mon Sep 17 00:00:00 2001
From: hbyang <hbyang@o2o.kr>
Date: Fri, 12 Jun 2026 17:33:59 +0900
Subject: [PATCH] Diversify AI predictions via per-model analyst personas

GPT (data-driven), Claude (tactical/upset-aware), Gemini (attacking-minded)
each get a distinct perspective so the 3 picks genuinely diverge instead of
producing identical scorelines. Honest: still each model's own judgment.
Finished matches are untouched (generation filters result_outcome IS NULL).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---
 backend/app/services/ai.py | 35 +++++++++++++++++++++++++++++------
 1 file changed, 29 insertions(+), 6 deletions(-)

diff --git a/backend/app/services/ai.py b/backend/app/services/ai.py
index 4257404..4f41c3a 100644
--- a/backend/app/services/ai.py
+++ b/backend/app/services/ai.py
@@ -32,10 +32,33 @@ class MatchContext:
     kickoff: str  # ISO
 
 
-def _prompt(ctx: MatchContext) -> str:
+# 모델별 분석 관점(페르소나) — 동일 경기라도 서로 다른 시각으로 보게 해
+# 예측이 자연스럽게 갈리도록 한다(강제 분산이 아니라 진짜 판단의 다양화).
+PERSONA = {
+    "GPT": (
+        "You are a DATA-DRIVEN analyst. Base your call on recent form, head-to-head, "
+        "FIFA ranking gaps and goals-scored/conceded trends. Be objective and "
+        "evidence-led; pick the scoreline the numbers most support."
+    ),
+    "Claude": (
+        "You are a TACTICAL analyst. Focus on matchups, defensive organization, "
+        "midfield control and game-state. Take low-scoring games, tight margins and "
+        "genuine upset potential seriously — do not just rubber-stamp the favorite."
+    ),
+    "Gemini": (
+        "You are an ATTACKING-MINDED analyst. Weigh momentum, attacking quality, "
+        "star players and scoring potential. Lean toward open, higher-scoring "
+        "scenarios when the talent and tempo justify it."
+    ),
+}
+
+
+def _prompt(ctx: MatchContext, persona: str) -> str:
     return (
-        f"You are a football match analyst. Predict the result of this "
-        f"2026 FIFA World Cup match.\n"
+        f"{persona}\n"
+        f"Predict the result of this 2026 FIFA World Cup match using YOUR perspective "
+        f"above. Judge independently — it is fine to differ from the obvious consensus "
+        f"pick when your perspective warrants it.\n"
         f"Team A: {ctx.team_a}\nTeam B: {ctx.team_b}\n"
         f"Venue: {ctx.venue}\nKickoff: {ctx.kickoff}\n"
         f"Important: World Cup group-stage matches are played at NEUTRAL venues. "
@@ -96,7 +119,7 @@ async def predict_gpt(ctx: MatchContext) -> dict:
         model=settings.openai_model,
         messages=[
             {"role": "system", "content": "You output only valid JSON."},
-            {"role": "user", "content": _prompt(ctx)},
+            {"role": "user", "content": _prompt(ctx, PERSONA["GPT"])},
         ],
         response_format={"type": "json_object"},
     )
@@ -116,7 +139,7 @@ async def predict_claude(ctx: MatchContext) -> dict:
         return await client.messages.create(
             model=settings.anthropic_model,
             max_tokens=1024,
-            messages=[{"role": "user", "content": _prompt(ctx)}],
+            messages=[{"role": "user", "content": _prompt(ctx, PERSONA["Claude"])}],
             **extra,
         )
 
@@ -143,7 +166,7 @@ async def predict_gemini(ctx: MatchContext) -> dict:
     client = genai.Client(api_key=settings.google_api_key)
     resp = await client.aio.models.generate_content(
         model=settings.google_model,
-        contents=_prompt(ctx),
+        contents=_prompt(ctx, PERSONA["Gemini"]),
         config=types.GenerateContentConfig(response_mime_type="application/json"),
     )
     return _normalize(json.loads(resp.text or "{}"))