diff --git a/dev_gpt/options/generate/chains/question_answering.py b/dev_gpt/options/generate/chains/question_answering.py
index bd8dabe..34e4795 100644
--- a/dev_gpt/options/generate/chains/question_answering.py
+++ b/dev_gpt/options/generate/chains/question_answering.py
@@ -19,21 +19,26 @@ def answer_yes_no_question(text, question):
         question=question,
         text=text,
     )
+    # count words "yes" and "no" in pros and cons
+    yes_count = pros_and_cons.lower().count('yes')
+    no_count = pros_and_cons.lower().count('no')
+    return yes_count > no_count
 
-    return ask_gpt(
-        question_prompt,
-        boolean_parser,
-        text=text,
-        question=question,
-        pros_and_cons=pros_and_cons,
-    )
+    # return ask_gpt(
+    #     question_prompt,
+    #     boolean_parser,
+    #     text=text,
+    #     question=question,
+    #     pros_and_cons=pros_and_cons,
+    # )
 
 pros_and_cons_prompt = '''\
 # Context
 {text}
 # Question
 {question}
-Note: You must not answer the question. Instead, give up to 5 bullet points (10 words) arguing why the question should be answered with true or false.'''
+Note: You must not answer the question. Instead, give up to 5 bullet points (10 words) arguing why the question should be answered with yes or no. \
+Each bullet point ends with either "- yes" or "- no".'''
 
 question_prompt = '''\
 # Context
diff --git a/test/unit/test_yes_no_question.py b/test/unit/test_yes_no_question.py
new file mode 100644
index 0000000..e03d5d2
--- /dev/null
+++ b/test/unit/test_yes_no_question.py
@@ -0,0 +1,21 @@
+import os
+
+from dev_gpt.apis.gpt import GPTSession
+from dev_gpt.options.generate.chains.question_answering import answer_yes_no_question
+
+
+def test_answer_yes_no_question(tmpdir):
+    os.environ['VERBOSE'] = 'true'
+    GPTSession(os.path.join(str(tmpdir), 'log.json'), model='gpt-3.5-turbo')
+    assert answer_yes_no_question(
+        '''\
+Microservice description:
+```
+The microservice takes a stock symbol as input and returns a summary of the company's stock performance over the past 30 days, \
+including the average closing price and the company name. \
+The summary is returned as a string. \
+The microservice uses the Yahoo Finance API to fetch the stock data and Python libraries to calculate the average closing price and generate the summary. \
+The request parameter is "stock_symbol" and the response parameter is "summary".
+```
+''', 'Does the microservice send requests to an API beside the google_custom_search and gpt_3_5_turbo?'
+    ) == True
\ No newline at end of file