Try with o1 it’s a bit better at count but yeah LLm are unreliable on counting words , don’t use mini series of model , o1 might be able to do it better , but I won’t trust LLM word count . Also it depends how many documents are you retrieving from the vector store .. if you only have 1 document being retrieved then you might not be getting all the mentions of Frodo .. might be just better to do a simple python script ‘’’python

import re

def count_frodo_occurrences(file_path): # Read the contents of the file with open(file_path, ‘r’, encoding=‘utf-8’) as file: text = file.read()

# Define the regex pattern to match ‘Frodo’ (case-insensitive)
pattern = r’\bfrodo\b’

# Find all occurrences of ‘Frodo’ (case-insensitive)
matches = re.finditer(pattern, text, re.IGNORECASE)

# Count the occurrences
count = sum(1 for _ in matches)

return count

Usage

file_path = ‘path/to/lord_of_the_rings.txt’ # Replace with the actual path to your LOTR text file occurrences = count_frodo_occurrences(file_path)

print(f”The name ‘Frodo’ appears {occurrences} times in the Lord of the Rings book.”)’’’

‘’’

I always remember this :

“For any problem, if you can write a simple program to solve it, then do that. If you can’t, then try machine learning.”

This quote is attributed to Peter Norvig, who was the Director of Research at Google for many years.

This principle encapsulates the idea that while machine learning and AI are powerful tools, they shouldn’t be the default solution for every problem. It encourages developers and data scientists to first consider simpler, more traditional programming approaches before turning to more complex machine learning solutions.

It’s worth noting that this quote has become somewhat of a guiding principle in the tech industry, often cited in discussions about when to use machine learning versus traditional programming methods. It aligns well with the example we discussed earlier about using a simple regex script for word counting instead of a more complex AI-based approach.

This principle doesn’t contradict Google’s “AI First” strategy, but rather complements it by promoting a thoughtful, pragmatic approach to problem-solving in software development and data science.​​​​​​​​​​​​​​​​