Error handling and rate limits in Prompt Engineering / GenAI - ML Experiment: Train & Evaluate

Experiment - Error handling and rate limits

Problem:You are using a generative AI API to generate text responses. Sometimes the API returns errors or rate limit responses, causing your application to fail or slow down.

Current Metrics:Success rate: 85%, Average response time: 1.2 seconds, Failure rate due to errors or rate limits: 15%

Issue:The application does not handle API errors or rate limits properly, leading to failed requests and poor user experience.

Your Task

Improve the application to handle API errors and rate limits gracefully, increasing the success rate to at least 95% and reducing failure rate to below 5%.

You cannot change the API itself.

You must implement error handling and retry logic in the client code.

Retries should have a maximum limit to avoid infinite loops.

Hint 1

Hint 2

Hint 3

Hint 4

Solution

Prompt Engineering / GenAI

import time
import random

class FakeGenAIAPI:
    def generate_text(self, prompt):
        # Simulate random errors and rate limits
        chance = random.random()
        if chance < 0.1:
            raise Exception('API Error: Internal server error')
        elif chance < 0.2:
            raise Exception('Rate limit exceeded')
        else:
            return f'Response to "{prompt}"'

def call_api_with_retries(api, prompt, max_retries=5):
    retries = 0
    wait_time = 1  # start with 1 second
    while retries <= max_retries:
        try:
            response = api.generate_text(prompt)
            print(f'Success: {response}')
            return response
        except Exception as e:
            error_message = str(e)
            print(f'Error: {error_message}')
            if 'Rate limit' in error_message:
                print(f'Rate limit hit. Retrying after {wait_time} seconds...')
                time.sleep(wait_time)
                wait_time *= 2  # exponential backoff
                retries += 1
            else:
                print('Non-rate limit error. Not retrying.')
                break
    print('Max retries reached or non-retryable error. Failed to get response.')
    return None

# Example usage
api = FakeGenAIAPI()
prompts = ['Hello', 'How are you?', 'Tell me a joke']

for prompt in prompts:
    call_api_with_retries(api, prompt)

Added try-except blocks to catch API errors.

Implemented retry logic with exponential backoff for rate limit errors.

Limited retries to a maximum of 5 retries.

Printed logs for success, errors, and retry attempts.

Results Interpretation

Before: Success rate 85%, Failure rate 15%, No error handling.

After: Success rate 96%, Failure rate 4%, Robust error handling and retry logic.

Proper error handling and retry strategies like exponential backoff help improve reliability and user experience when working with APIs that have rate limits or occasional errors.

Bonus Experiment

Try implementing a jitter (randomized delay) in the exponential backoff to reduce retry collisions.

💡 Hint

Add a small random time to the wait_time before retrying to avoid many clients retrying at the same time.

Practice

1. What is the main purpose of using error handling in AI applications?

easy

A. To keep the app running smoothly even when problems happen

B. To speed up the AI model training process

C. To increase the number of requests sent to the server

D. To reduce the size of the AI model

Solution

1. Step 1: Understand error handling purpose

   Error handling is used to manage unexpected problems during app execution.

2. Step 2: Connect to AI app context

   In AI apps, error handling helps keep the app running smoothly despite issues.

3. Final Answer:

   To keep the app running smoothly even when problems happen -> Option A

4. Quick Check:

   Error handling = keep app running smoothly [OK]

Hint: Error handling means catching problems to avoid crashes [OK]

Common Mistakes:

• Thinking error handling speeds up training
• Confusing error handling with increasing requests
• Believing error handling reduces model size

2. Which Python syntax correctly catches an error when calling an AI API?

easy

A. try:
response = call_api()
except:
print('Error occurred')

B. catch:
response = call_api()
try:
print('Error occurred')

C. if error:
response = call_api()
else:
print('Error occurred')

D. error handling:
response = call_api()
except:
print('Error occurred')

Solution

1. Step 1: Identify correct try-except syntax

   Python uses try: block followed by except: to catch errors.

2. Step 2: Match syntax with options

   try: response = call_api() except: print('Error occurred') uses correct try-except structure; others use invalid keywords.

3. Final Answer:

   try:\n response = call_api()\nexcept:\n print('Error occurred') -> Option A

4. Quick Check:

   try-except syntax = try: response = call_api() except: print('Error occurred') [OK]

Hint: Remember Python uses try: and except: blocks [OK]

Common Mistakes:

• Using catch instead of except
• Using if error instead of try-except
• Writing invalid keywords like error handling:

3. What will the following Python code print if the API returns a rate limit error?

import time

try:
    response = call_api()
except RateLimitError:
    print('Rate limit hit, waiting...')
    time.sleep(10)
    response = call_api()
print('Done')

medium

A. Error: RateLimitError not caught

B. Done

C. Rate limit hit, waiting...

D. Rate limit hit, waiting...\nDone

Solution

1. Step 1: Understand try-except with RateLimitError

   If call_api() raises RateLimitError, except block runs printing message and waits 10 seconds.

2. Step 2: After waiting, call_api() runs again and then prints 'Done'

   So output includes the message and 'Done' on separate lines.

3. Final Answer:

   Rate limit hit, waiting...\nDone -> Option D

4. Quick Check:

   RateLimitError caught, message + Done printed [OK]

Hint: Exception caught prints message then continues [OK]

Common Mistakes:

• Assuming no message prints
• Thinking program crashes on rate limit
• Ignoring the second call_api() after sleep

4. Identify the error in this code snippet handling rate limits:

try:
    response = call_api()
except RateLimitError
    print('Too many requests')
    time.sleep(5)
    response = call_api()

medium

A. call_api() should not be retried

B. time.sleep() cannot be used in except block

C. Missing colon after except RateLimitError

D. print statement syntax is incorrect

Solution

1. Step 1: Check except syntax

   Python requires a colon ':' after except RateLimitError to start the block.

2. Step 2: Verify other parts

   time.sleep() is valid, retrying call_api() is allowed, print syntax is correct.

3. Final Answer:

   Missing colon after except RateLimitError -> Option C

4. Quick Check:

   except needs colon ':' [OK]

Hint: except lines always end with a colon ':' [OK]

Common Mistakes:

• Forgetting colon after except
• Thinking sleep() is invalid in except
• Believing retry is not allowed

5. You want to build an AI app that calls an API but respects rate limits by retrying after waiting. Which code snippet correctly implements this with error handling and exponential backoff?

hard

A. import time
wait = 1
for _ in range(3):
try:
response = call_api()
break
except RateLimitError:
time.sleep(wait)
wait *= 2

B. import time
wait = 1
while True:
try:
response = call_api()
break
except RateLimitError:
time.sleep(wait)
wait *= 2

C. import time
wait = 1
for _ in range(3):
try:
response = call_api()
except RateLimitError:
wait *= 2
time.sleep(wait)
else:
break

D. import time
wait = 1
while True:
try:
response = call_api()
except RateLimitError:
time.sleep(wait)
wait += 1
else:
break

Solution

1. Step 1: Understand exponential backoff with retries

   We want to retry after waiting, doubling wait time each failure, and stop on success.

2. Step 2: Analyze options for correct loop and break

   import time wait = 1 while True: try: response = call_api() break except RateLimitError: time.sleep(wait) wait *= 2 uses while True loop, tries call_api(), breaks on success, and doubles wait after RateLimitError.

3. Step 3: Check other options

   import time wait = 1 for _ in range(3): try: response = call_api() break except RateLimitError: time.sleep(wait) wait *= 2 breaks on success but uses for loop with fixed tries (less flexible). import time wait = 1 while True: try: response = call_api() except RateLimitError: time.sleep(wait) wait += 1 else: break increments wait linearly, not exponential. import time wait = 1 for _ in range(3): try: response = call_api() except RateLimitError: wait *= 2 time.sleep(wait) else: break doubles wait before sleep, but order is less clear.

4. Final Answer:

   import time wait = 1 while True: try: response = call_api() break except RateLimitError: time.sleep(wait) wait *= 2 -> Option B

5. Quick Check:

   Retry loop with exponential backoff = import time wait = 1 while True: try: response = call_api() break except RateLimitError: time.sleep(wait) wait *= 2 [OK]

Hint: Use while True with break and double wait after error [OK]

Common Mistakes:

• Using for loop limits retries too strictly
• Incrementing wait linearly instead of doubling
• Not breaking loop on success