diff --git a/backend/main.py b/backend/main.py
index 1f453ab..c018862 100644
--- a/backend/main.py
+++ b/backend/main.py
@@ -822,22 +822,16 @@ class Session:
             "recent_caller_summaries": self._get_recent_summaries(),
             "voice_roster": voice_roster,
         }
-        # Split into two parallel calls: call 1 includes regulars + 5 walk-ins,
-        # call 2 is 6 pure walk-ins. Halves generation time by parallelizing.
-        ctx_a = {**base_ctx, "regulars_included": regulars_for_tonight, "caller_count": 6}
-        ctx_b = {**base_ctx, "regulars_included": [], "caller_count": 6}
-
-        batches = await asyncio.gather(
-            caller_gen.generate_batch(ctx_a),
-            caller_gen.generate_batch(ctx_b),
-            return_exceptions=True,
-        )
-        identities: list = []
-        for i, result in enumerate(batches):
-            if isinstance(result, Exception):
-                print(f"[Background] Batch {i+1} failed: {result}")
-                continue
-            identities.extend(result)
+        # Single batch of 10 so sonnet sees the full roster and can enforce
+        # the anti-collision rule across all callers. Previously two parallel
+        # batches of 6 were generated blind to each other, which produced
+        # recurring archetype clusters (e.g. two BBQ callers, two taxidermists).
+        ctx = {**base_ctx, "regulars_included": regulars_for_tonight, "caller_count": 10}
+        try:
+            identities = await caller_gen.generate_batch(ctx)
+        except Exception as e:
+            print(f"[Background] Batch failed: {e}")
+            identities = []
 
         # Build regular lookup tables
         regular_names = {r["name"] for r in regulars_for_tonight}
diff --git a/backend/services/caller_gen.py b/backend/services/caller_gen.py
index aada1e4..f7c03b3 100644
--- a/backend/services/caller_gen.py
+++ b/backend/services/caller_gen.py
@@ -62,7 +62,11 @@ BATCH_SYSTEM_PROMPT = """You are writing a roster of callers for Luke's late-nig
 
 CREATIVE RANGE: Your callers must span the emotional range of Howard Stern (chaos, strong characters), Coast to Coast AM (earnest weirdos, sincere believers), Loveline (real problems, real advice-seeking), Delilah (emotional vulnerability, connection), and Opie and Anthony (sharp, irreverent, specific people).
 
-Maximum character distance between callers. No two callers should feel like siblings. Do not default to sitcom plots. Real humans are specific and strange. Give each caller details that could only belong to them.
+Maximum character distance between callers. No two callers should feel like siblings.
+
+ANTI-COLLISION RULE — THIS IS NON-NEGOTIABLE: All callers in this roster must be clearly differentiated. No two callers may share the same hobby, obsession, profession archetype, or story theme. Specifically forbidden within a single roster: two BBQ competitors, two taxidermists, two amateur-radio or mystery-signal callers, two callers with ex-spouse drama, two conspiracy/pattern-seers, two retired military, two grandmothers-of-many, two callers calling about a weird neighbor, two callers with religious-object stories. Each caller's defining "thing" — their hook, their obsession, the specific topic they're calling about — must appear exactly once in the roster. Before finalizing, scan your output and swap any collisions.
+
+Do not default to sitcom plots. Real humans are specific and strange. Give each caller details that could only belong to them.
 
 You will output strict JSON with a "callers" array. Each caller has exactly these fields: name, age, voice_suggestion, location, identity, situation, reason_calling, opening_line, secret_want, specific_details (array of 2-3 strings), emotional_register."""
 
@@ -113,7 +117,7 @@ async def generate_batch(ctx: dict) -> list[CallerIdentity]:
                 "model": BATCH_MODEL,
                 "messages": [{"role": "user", "content": prompt}],
                 "response_format": {"type": "json_object"},
-                "max_tokens": 8000,
+                "max_tokens": 16000,
                 "temperature": 0.9,
             },
         )