diff --git a/packages/gooddata-eval/src/gooddata_eval/core/agentic/metric_skill.py b/packages/gooddata-eval/src/gooddata_eval/core/agentic/metric_skill.py
index 2e2b5b9b1..35bee3975 100644
--- a/packages/gooddata-eval/src/gooddata_eval/core/agentic/metric_skill.py
+++ b/packages/gooddata-eval/src/gooddata_eval/core/agentic/metric_skill.py
@@ -88,13 +88,16 @@ def generate_simulated_response(agent_message: str, expected_output: dict) -> st
     prompt = (
         f"You are simulating a user in a conversation with a BI assistant that creates metrics. "
         f"The assistant said: '{agent_message}'. "
-        f"The user originally asked to create a metric with MAQL: {expected_maql}. "
-        f"Reply briefly as the user, providing any clarification the assistant needs."
+        f"The user's ground-truth intended metric is exactly this MAQL: {expected_maql}. "
+        f"Reply as the user. You MUST ensure every clause of that MAQL (including any WHERE/filter "
+        f"conditions) is eventually satisfied, and quote field/label identifiers verbatim from it -- "
+        f"never paraphrase or drop a clause, even if the assistant's question doesn't explicitly ask "
+        f"about it. If the assistant's offered options omit a required filter, add it yourself."
     )
     response = client.chat.completions.create(
         model="gpt-4o-mini",
         messages=[{"role": "user", "content": prompt}],
-        max_tokens=150,
+        max_tokens=300,
     )
     return response.choices[0].message.content or "Please proceed."
 
diff --git a/packages/gooddata-eval/tests/test_agentic_metric_skill.py b/packages/gooddata-eval/tests/test_agentic_metric_skill.py
index 67a163e92..3de90cdf5 100644
--- a/packages/gooddata-eval/tests/test_agentic_metric_skill.py
+++ b/packages/gooddata-eval/tests/test_agentic_metric_skill.py
@@ -1,5 +1,7 @@
 # (C) 2026 GoodData Corporation. All rights reserved.
 # SPDX-License-Identifier: LicenseRef-GoodData-Enterprise
+import sys
+import types
 from unittest.mock import MagicMock, patch
 
 import pytest
@@ -8,6 +10,7 @@
     MetricRunResult,
     _delete_metric,
     _normalize_maql,
+    generate_simulated_response,
     run_agentic_metric_skill,
 )
 from gooddata_eval.core.models import ChatResult
@@ -21,6 +24,39 @@ def test_normalize_maql_removes_select_wrapper():
     assert _normalize_maql("(SELECT {metric/abc})") == "{metric/abc}"
 
 
+def test_generate_simulated_response_prompt_preserves_maql_fidelity(monkeypatch):
+    """Regression test for a live-reproduced bug: the old prompt ("reply briefly",
+    no instruction to cover clauses the assistant didn't ask about) let the
+    simulating LLM silently drop a MAQL's WHERE clause or paraphrase a label id --
+    confirmed via a 5x-repeated A/B test (1/5 vs 5/5 fidelity) that this was the
+    prompt, not the model (gpt-4o did not fix it under the old prompt either).
+    """
+    monkeypatch.setenv("OPENAI_API_KEY", "test-key")
+    mock_client = MagicMock()
+    mock_response = MagicMock()
+    mock_response.choices = [MagicMock(message=MagicMock(content="ok"))]
+    mock_client.chat.completions.create.return_value = mock_response
+
+    # `openai` is an optional [llm-judge] extra, not installed in this test env --
+    # inject a fake module rather than patching a real one (mirrors how the source
+    # itself does `from openai import OpenAI` as a local, guarded import).
+    fake_openai_module = types.SimpleNamespace(OpenAI=MagicMock(return_value=mock_client))
+    monkeypatch.setitem(sys.modules, "openai", fake_openai_module)
+
+    expected_output = {"maql": 'SELECT {metric/spend_amount_-_cutcgco} WHERE {label/ecommerce_indicator_code} = "1"'}
+    generate_simulated_response("Which base metric should I use?", expected_output)
+
+    call_kwargs = mock_client.chat.completions.create.call_args.kwargs
+    sent_prompt = call_kwargs["messages"][0]["content"]
+
+    assert expected_output["maql"] in sent_prompt
+    assert "verbatim" in sent_prompt
+    assert "every clause" in sent_prompt
+    assert "WHERE" in sent_prompt or "filter" in sent_prompt.lower()
+    assert "reply briefly" not in sent_prompt.lower()
+    assert call_kwargs["max_tokens"] >= 300
+
+
 def test_metric_run_result_fields():
     r = MetricRunResult(
         conversation_id="c1",