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Agentic AIml~5 mins

Token usage and cost tracking in Agentic AI - Cheat Sheet & Quick Revision

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Recall & Review
beginner
What is a token in the context of AI language models?
A token is a small piece of text, like a word or part of a word, that the AI model reads and processes to understand and generate language.
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beginner
Why is tracking token usage important when using AI models?
Tracking token usage helps you understand how much text you send and receive, which affects the cost and efficiency of using AI services.
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intermediate
How does token usage relate to cost in AI services?
AI services often charge based on the number of tokens processed. More tokens mean higher cost, so managing token usage helps control expenses.
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intermediate
What are common methods to track token usage in AI applications?
Common methods include using built-in API usage reports, logging tokens in your code, and setting limits or alerts to monitor usage.
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intermediate
How can you reduce token usage to save costs?
You can reduce token usage by shortening inputs, avoiding unnecessary text, using concise prompts, and limiting output length.
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What does a token usually represent in AI language models?
AA whole paragraph
BA single character only
CA piece of text like a word or part of a word
DAn image file
Why should you track token usage when using AI APIs?
ATo understand and control costs
BTo increase the number of tokens used
CTo make the model slower
DTo avoid using the AI service
Which of these actions can help reduce token usage?
ARequesting longer outputs
BAdding more unnecessary text
CIgnoring token counts
DUsing shorter prompts
How do AI services usually charge for usage?
ABy the number of API calls only
BBased on the number of tokens processed
CBy the time of day
DBy the number of users
Which tool can help you monitor token usage?
AAPI usage reports
BRandom guessing
CIgnoring logs
DTurning off the computer
Explain what tokens are and why tracking their usage matters in AI applications.
Think about how AI reads text and how costs add up.
You got /3 concepts.
    Describe practical ways to reduce token usage and save money when using AI models.
    Focus on how to use less text and keep track of it.
    You got /4 concepts.

      Practice

      (1/5)
      1. What is a token in the context of AI language models?
      easy
      A. A programming language used for AI
      B. A type of AI model architecture
      C. A small piece of text like a word or part of a word
      D. A hardware component for AI processing

      Solution

      1. Step 1: Understand the definition of a token

        A token is a small piece of text that AI models use to understand language. It can be a word or part of a word.

      2. Step 2: Differentiate tokens from other AI terms

        Tokens are not models, programming languages, or hardware. They are units of text input.

      3. Final Answer:

        A small piece of text like a word or part of a word -> Option C

      4. Quick Check:

        Token = small text piece [OK]
      Hint: Tokens are text pieces, not models or hardware [OK]
      Common Mistakes:
      • Confusing tokens with AI models
      • Thinking tokens are programming languages
      • Assuming tokens are hardware parts
      2. Which of the following is the correct way to track token usage in a Python script using an AI API?
      easy
      A. tokens_used = response['total_tokens']
      B. tokens_used = response['usage']['total_tokens']
      C. tokens_used = response['usage']['tokens']
      D. tokens_used = response['token_count']

      Solution

      1. Step 1: Identify the correct key for token usage in API response

        Most AI APIs return token usage under response['usage']['total_tokens'].

      2. Step 2: Compare options with common API response structure

        Only tokens_used = response['usage']['total_tokens'] matches the standard nested key for total tokens used.

      3. Final Answer:

        tokens_used = response['usage']['total_tokens'] -> Option B

      4. Quick Check:

        Correct key path = response['usage']['total_tokens'] [OK]
      Hint: Look for nested 'usage' then 'total_tokens' key [OK]
      Common Mistakes:
      • Using wrong key names like 'token_count'
      • Missing nested 'usage' dictionary
      • Assuming flat keys for token counts
      3. Given the following code snippet, what will be printed? 
      response = {'usage': {'prompt_tokens': 50, 'completion_tokens': 30, 'total_tokens': 80}}
print(response['usage']['total_tokens'])
      medium
      A. 80
      B. 30
      C. 50
      D. Error

      Solution

      1. Step 1: Access the 'total_tokens' key in the nested dictionary

        The code accesses response['usage']['total_tokens'], which is 80.

      2. Step 2: Confirm the print output

        Printing this value outputs 80 without error.

      3. Final Answer:

        80 -> Option A

      4. Quick Check:

        response['usage']['total_tokens'] = 80 [OK]
      Hint: Check nested dictionary keys carefully [OK]
      Common Mistakes:
      • Confusing prompt_tokens with total_tokens
      • Mixing completion_tokens with total_tokens
      • Assuming code causes error
      4. You wrote this code to track token usage but get a KeyError: 
      tokens = response['usage']['token_total']
print(tokens)
      What is the likely cause?
      medium
      A. The 'usage' key is missing in the response
      B. The response variable is not defined
      C. The print statement syntax is incorrect
      D. The key 'token_total' does not exist in the response dictionary

      Solution

      1. Step 1: Check the key names in the response dictionary

        The correct key is 'total_tokens', not 'token_total'.

      2. Step 2: Understand KeyError cause

        Using a wrong key name causes KeyError because that key does not exist.

      3. Final Answer:

        The key 'token_total' does not exist in the response dictionary -> Option D

      4. Quick Check:

        Wrong key name causes KeyError [OK]
      Hint: Verify exact key names in API response [OK]
      Common Mistakes:
      • Assuming similar key names exist
      • Ignoring case sensitivity in keys
      • Thinking print syntax causes error
      5. You want to estimate the cost of an AI request. The model charges $0.002 per 1000 tokens. If your request uses 2500 tokens, what is the total cost?
      hard
      A. $0.005
      B. $0.0025
      C. $0.05
      D. $0.0005

      Solution

      1. Step 1: Calculate cost per token

        Cost per token = $0.002 / 1000 = $0.000002 per token.

      2. Step 2: Multiply by number of tokens used

        Total cost = 2500 tokens * $0.000002 = $0.005.

      3. Final Answer:

        $0.005 -> Option A

      4. Quick Check:

        2500 tokens x $0.002/1000 = $0.005 [OK]
      Hint: Multiply tokens by cost per token (divide by 1000 first) [OK]
      Common Mistakes:
      • Multiplying by 0.002 directly without dividing by 1000
      • Using wrong token count
      • Confusing decimals in cost calculation