Tutorial 8
Evaluate LLM responses when there is a single “right answer”.
You need to download the following files:
List of products and categories that are in the product catalog.
products_and_category = {'Computers and Laptops': ['TechPro Ultrabook',
'BlueWave Gaming Laptop',
'PowerLite Convertible',
'TechPro Desktop',
'BlueWave Chromebook'],
'Smartphones and Accessories': ['SmartX ProPhone',
'MobiTech PowerCase',
'SmartX MiniPhone',
'MobiTech Wireless Charger',
'SmartX EarBuds'],
'Televisions and Home Theater Systems': ['CineView 4K TV',
'SoundMax Home Theater',
'CineView 8K TV',
'SoundMax Soundbar',
'CineView OLED TV'],
'Gaming Consoles and Accessories': ['GameSphere X',
'ProGamer Controller',
'GameSphere Y',
'ProGamer Racing Wheel',
'GameSphere VR Headset'],
'Audio Equipment': ['AudioPhonic Noise-Canceling Headphones',
'WaveSound Bluetooth Speaker',
'AudioPhonic True Wireless Earbuds',
'WaveSound Soundbar',
'AudioPhonic Turntable'],
'Cameras and Camcorders': ['FotoSnap DSLR Camera',
'ActionCam 4K',
'FotoSnap Mirrorless Camera',
'ZoomMaster Camcorder',
'FotoSnap Instant Camera']}This could be the version that is running in production.
def find_category_and_product_v1(user_input, products_and_category):
delimiter = "####"
system_message = f"""
You will be provided with customer service queries. \
The customer service query will be delimited with {delimiter} characters.
Output a python list of json objects, where each object has the following format:
'category': <one of Computers and Laptops, Smartphones and Accessories, Televisions and Home Theater Systems, \
Gaming Consoles and Accessories, Audio Equipment, Cameras and Camcorders>,
AND
'products': <a list of products that must be found in the allowed products below>
Where the categories and products must be found in the customer service query.
If a product is mentioned, it must be associated with the correct category in the allowed products list below.
If no products or categories are found, output an empty list.
List out all products that are relevant to the customer service query based on how closely it relates
to the product name and product category.
Do not assume, from the name of the product, any features or attributes such as relative quality or price.
The allowed products are provided in JSON format.
The keys of each item represent the category.
The values of each item is a list of products that are within that category.
Allowed products: {products_and_category}
"""
few_shot_user_1 = """I want the most expensive computer."""
few_shot_assistant_1 = """
[{'category': 'Computers and Laptops', \
'products': ['TechPro Ultrabook', 'BlueWave Gaming Laptop', 'PowerLite Convertible', 'TechPro Desktop', 'BlueWave Chromebook']}]
"""
messages = [
{'role': 'system', 'content': system_message},
{'role': 'user', 'content': f"{delimiter}{few_shot_user_1}{delimiter}"},
{'role': 'assistant', 'content': few_shot_assistant_1},
{'role': 'user', 'content': f"{delimiter}{user_input}{delimiter}"},
]
return get_completion_from_messages(messages)Identify queries found in production, where the model is not working as expected.
Modify the prompt to work on the hard test cases
def find_category_and_product_v2(user_input, products_and_category):
"""
Added: Do not output any additional text that is not in JSON format.
Added a second example (for few-shot prompting) where user asks for the cheapest computer. In both few-shot examples, the shown response is the full list of products in JSON only.
"""
delimiter = "####"
system_message = f"""
You will be provided with customer service queries. \
The customer service query will be delimited with {delimiter} characters.
Output a python list of json objects, where each object has the following format:
'category': <one of Computers and Laptops, Smartphones and Accessories, Televisions and Home Theater Systems, \
Gaming Consoles and Accessories, Audio Equipment, Cameras and Camcorders>,
AND
'products': <a list of products that must be found in the allowed products below>
Do not output any additional text that is not in JSON format.
Do not write any explanatory text after outputting the requested JSON.
Where the categories and products must be found in the customer service query.
If a product is mentioned, it must be associated with the correct category in the allowed products list below.
If no products or categories are found, output an empty list.
List out all products that are relevant to the customer service query based on how closely it relates to the product name and product category.
Do not assume, from the name of the product, any features or attributes such as relative quality or price.
The allowed products are provided in JSON format.
The keys of each item represent the category.
The values of each item is a list of products that are within that category.
Allowed products: {products_and_category}
"""
few_shot_user_1 = """I want the most expensive computer. What do you recommend?"""
few_shot_assistant_1 = """
[{'category': 'Computers and Laptops', \
'products': ['TechPro Ultrabook', 'BlueWave Gaming Laptop', 'PowerLite Convertible', 'TechPro Desktop', 'BlueWave Chromebook']}]
"""
few_shot_user_2 = """I want the most cheapest computer. What do you recommend?"""
few_shot_assistant_2 = """
[{'category': 'Computers and Laptops', \
'products': ['TechPro Ultrabook', 'BlueWave Gaming Laptop', 'PowerLite Convertible', 'TechPro Desktop', 'BlueWave Chromebook']}]
"""
messages = [
{'role': 'system', 'content': system_message},
{'role': 'user', 'content': f"{delimiter}{few_shot_user_1}{delimiter}"},
{'role': 'assistant', 'content': few_shot_assistant_1},
{'role': 'user', 'content': f"{delimiter}{few_shot_user_2}{delimiter}"},
{'role': 'assistant', 'content': few_shot_assistant_2},
{'role': 'user', 'content': f"{delimiter}{user_input}{delimiter}"},
]
return get_completion_from_messages(messages)Evaluate the modified prompt on the hard tests cases
Verify that the model still works on previous test cases
Check that modifying the model to fix the hard test cases does not negatively affect its performance on previous test cases.
Gather development set for automated testin
msg_ideal_pairs_set = [
# eg 0
{'customer_msg': """Which TV can I buy if I'm on a budget?""",
'ideal_answer': {
'Televisions and Home Theater Systems': set(
['CineView 4K TV', 'SoundMax Home Theater', 'CineView 8K TV',
'SoundMax Soundbar', 'CineView OLED TV']
)}
},
# eg 1
{'customer_msg': """I need a charger for my smartphone""",
'ideal_answer': {
'Smartphones and Accessories': set(
['MobiTech PowerCase', 'MobiTech Wireless Charger', 'SmartX EarBuds']
)}
},
# eg 2
{'customer_msg': f"""What computers do you have?""",
'ideal_answer': {
'Computers and Laptops': set(
['TechPro Ultrabook', 'BlueWave Gaming Laptop', 'PowerLite Convertible', 'TechPro Desktop', 'BlueWave Chromebook'
])
}
},
# eg 3
{'customer_msg': f"""tell me about the smartx pro phone and \
the fotosnap camera, the dslr one.\
Also, what TVs do you have?""",
'ideal_answer': {
'Smartphones and Accessories': set(
['SmartX ProPhone']),
'Cameras and Camcorders':set(
['FotoSnap DSLR Camera']),
'Televisions and Home Theater Systems':set(
['CineView 4K TV', 'SoundMax Home Theater', 'CineView 8K TV', 'SoundMax Soundbar', 'CineView OLED TV'])
}
},
# eg 4
{'customer_msg': """tell me about the CineView TV, the 8K one, Gamesphere console, the X one.
I'm on a budget, what computers do you have?""",
'ideal_answer': {
'Televisions and Home Theater Systems': set(
['CineView 8K TV']),
'Gaming Consoles and Accessories':set(
['GameSphere X']),
'Computers and Laptops':set(
['TechPro Ultrabook', 'BlueWave Gaming Laptop', 'PowerLite Convertible', 'TechPro Desktop', 'BlueWave Chromebook'])
}
},
# eg 5
{'customer_msg': f"""What smartphones do you have?""",
'ideal_answer': {
'Smartphones and Accessories': set(
['SmartX ProPhone', 'MobiTech PowerCase', 'SmartX MiniPhone', 'MobiTech Wireless Charger', 'SmartX EarBuds'
])
}
},
# eg 6
{'customer_msg': f"""I'm on a budget. Can you recommend some smartphones to me?""",
'ideal_answer': {
'Smartphones and Accessories': set(
['SmartX EarBuds', 'SmartX MiniPhone', 'MobiTech PowerCase',
'SmartX ProPhone', 'MobiTech Wireless Charger']
)}
},
# eg 7 # this will output a subset of the ideal answer
{'customer_msg': f"""What Gaming consoles would be good for my friend who is into racing games?""",
'ideal_answer': {
'Gaming Consoles and Accessories': set([
'GameSphere X',
'ProGamer Controller',
'GameSphere Y',
'ProGamer Racing Wheel',
'GameSphere VR Headset'
])}
},
# eg 8
{'customer_msg': f"""What could be a good present for my videographer friend?""",
'ideal_answer': {
'Cameras and Camcorders': set([
'FotoSnap DSLR Camera', 'ActionCam 4K', 'FotoSnap Mirrorless Camera', 'ZoomMaster Camcorder', 'FotoSnap Instant Camera'
])}
},
# eg 9
{'customer_msg': f"""I would like a hot tub time machine.""",
'ideal_answer': []
}
]Evaluate test cases by comparing to the ideal answers
def eval_response_with_ideal(response,
ideal,
debug=False):
if debug:
print("response")
print(response)
# json.loads() expects double quotes, not single quotes
json_like_str = response.replace("'", '"')
# parse into a list of dictionaries
l_of_d = json.loads(json_like_str)
# special case when response is empty list
if l_of_d == [] and ideal == []:
return 1
# otherwise, response is empty
# or ideal should be empty, there's a mismatch
elif l_of_d == [] or ideal == []:
return 0
correct = 0
if debug:
print("l_of_d is")
print(l_of_d)
for d in l_of_d:
cat = d.get('category')
prod_l = d.get('products')
if cat and prod_l:
# convert list to set for comparison
prod_set = set(prod_l)
# get ideal set of products
ideal_cat = ideal.get(cat)
if ideal_cat:
prod_set_ideal = set(ideal.get(cat))
else:
if debug:
print(f"did not find category {cat} in ideal")
print(f"ideal: {ideal}")
continue
if debug:
print("prod_set\n", prod_set)
print()
print("prod_set_ideal\n", prod_set_ideal)
if prod_set == prod_set_ideal:
if debug:
print("correct")
correct += 1
else:
print("incorrect")
print(f"prod_set: {prod_set}")
print(f"prod_set_ideal: {prod_set_ideal}")
if prod_set <= prod_set_ideal:
print("response is a subset of the ideal answer")
elif prod_set >= prod_set_ideal:
print("response is a superset of the ideal answer")
# count correct over total number of items in list
pc_correct = correct / len(l_of_d)
return pc_correctResonse:
[{‘category’: ‘Gaming Consoles and Accessories’, ‘products’: [‘GameSphere X’, ‘ProGamer Controller’, ‘GameSphere Y’, ‘ProGamer Racing Wheel’, ‘GameSphere VR Headset’]}]
1.0
Run evaluation on all test cases and calculate the fraction of cases that are correct
# Note, this will not work if any of the api calls time out
score_accum = 0
for i, pair in enumerate(msg_ideal_pairs_set):
print(f"example {i}")
customer_msg = pair['customer_msg']
ideal = pair['ideal_answer']
# print("Customer message",customer_msg)
# print("ideal:",ideal)
response = find_category_and_product_v2(customer_msg,
products_and_category)
# print("products_by_category",products_by_category)
score = eval_response_with_ideal(response, ideal, debug=False)
print(f"{i}: {score}")
score_accum += score
n_examples = len(msg_ideal_pairs_set)
fraction_correct = score_accum / n_examples
print(f"Fraction correct out of {n_examples}: {fraction_correct}")example 0 0: 1.0 example 1 incorrect prod_set: {‘MobiTech Wireless Charger’, ‘MobiTech PowerCase’, ‘SmartX MiniPhone’, ‘SmartX EarBuds’, ‘SmartX ProPhone’} prod_set_ideal: {‘MobiTech Wireless Charger’, ‘MobiTech PowerCase’, ‘SmartX EarBuds’} response is a superset of the ideal answer 1: 0.0 example 2 2: 1.0 example 3 incorrect prod_set: {‘CineView 4K TV’, ‘CineView 8K TV’, ‘CineView OLED TV’} prod_set_ideal: {‘CineView 8K TV’, ‘CineView OLED TV’, ‘SoundMax Home Theater’, ‘CineView 4K TV’, ‘SoundMax Soundbar’} response is a subset of the ideal answer 3: 0.6666666666666666 example 4 incorrect prod_set: {‘CineView 8K TV’, ‘CineView OLED TV’, ‘SoundMax Home Theater’, ‘CineView 4K TV’, ‘SoundMax Soundbar’} prod_set_ideal: {‘CineView 8K TV’} response is a superset of the ideal answer incorrect prod_set: {‘ProGamer Controller’, ‘GameSphere X’, ‘GameSphere VR Headset’, ‘GameSphere Y’, ‘ProGamer Racing Wheel’} prod_set_ideal: {‘GameSphere X’} response is a superset of the ideal answer 4: 0.3333333333333333 example 5 5: 1.0 example 6 6: 1.0 example 7 7: 1.0 example 8 8: 1.0 example 9 9: 1 Fraction correct out of 10: 0.8
This tutorial is mainly based on the excellent course “Building Systems with the ChatGPT API” provided by Isa Fulford from OpenAI and Andrew Ng from DeepLearning.AI
Congratulations! You have completed this tutorial 👍
Next, you may want to go back to the lab’s website
Jan Kirenz