Code generation in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of a simple code generation model snippet

What is the output of this Python code simulating a simple code generation model's token prediction?

Prompt Engineering / GenAI

tokens = ['def', ' ', 'add', '(', 'a', ',', ' ', 'b', ')', ':', '\n', '    ', 'return', ' ', 'a', ' ', '+', ' ', 'b']
predicted = ''.join(tokens)
print(predicted)

A

defadd(a,b):
returna+b

B def add(a, b): return a + b

C

def add(a, b):
    return a + b

D

def add(a, b):
return a + b

💡 Hint

Look carefully at the tokens and how they join with spaces and newlines.

The tokens include spaces and newline characters that format the function definition properly with indentation and line breaks.

❓ Model Choice

intermediate

Choosing the right model for code generation

Which model architecture is best suited for generating code snippets given a prompt?

A Transformer-based model

B Convolutional Neural Network (CNN)

C Recurrent Neural Network (RNN) with LSTM

D K-Nearest Neighbors (KNN)

💡 Hint

Consider models that handle long-range dependencies and context well.

Transformer models excel at capturing long-range dependencies and context, making them ideal for code generation tasks.

❓ Hyperparameter

advanced

Effect of temperature on code generation output

In a code generation model, what is the effect of increasing the temperature parameter during sampling?

A Improves the syntax correctness of generated code

B Generates more deterministic and repetitive code outputs

C Decreases the length of generated code

D Generates more random and diverse code outputs

💡 Hint

Think about how temperature affects randomness in sampling.

Higher temperature increases randomness, leading to more diverse but potentially less coherent outputs.

❓ Metrics

advanced

Evaluating code generation quality

Which metric is most appropriate to evaluate the functional correctness of generated code snippets?

A Number of tokens generated per second

B Code execution accuracy on test inputs

C Perplexity of the language model on generated code

D BLEU score comparing generated code to reference code

💡 Hint

Think about measuring if the code actually works as intended.

Execution accuracy tests if generated code produces correct outputs on inputs, directly measuring functional correctness.

🔧 Debug

expert

Debugging a code generation output error

A code generation model outputs this Python code snippet: "def multiply(a, b):\nreturn a * b" When running, it raises an IndentationError. What is the cause?

Prompt Engineering / GenAI

def multiply(a, b):
return a * b

A The return statement is not indented inside the function

B The function name is invalid

C The multiplication operator is incorrect

D The function is missing a colon at the end

💡 Hint

Python requires code blocks to be indented properly.

Python functions require the body to be indented; missing indentation causes IndentationError.

Practice

1. What is the main purpose of code generation in AI?

easy

A. Manually write code faster

B. Automatically create code from instructions

C. Run code without errors

D. Delete unnecessary code

Solution

1. Step 1: Understand code generation meaning

   Code generation means creating code automatically from instructions or examples.

2. Step 2: Match purpose with options

   Automatically create code from instructions correctly states this purpose, others describe different tasks.

3. Final Answer:

   Automatically create code from instructions -> Option B

4. Quick Check:

   Code generation = automatic code creation [OK]

Hint: Code generation means automatic code writing [OK]

Common Mistakes:

• Confusing code generation with manual coding
• Thinking code generation fixes errors automatically
• Believing code generation deletes code

2. Which of the following is the correct Python syntax to define a function named generate_code?

easy

A. generate_code def():

B. function generate_code()

C. def generate_code[]:

D. def generate_code():

Solution

1. Step 1: Recall Python function syntax

   Python functions start with def, followed by name and parentheses, then colon.

2. Step 2: Check each option

   def generate_code(): matches correct syntax; A, B and D have syntax errors (A wrong order, B JavaScript style, D brackets).

3. Final Answer:

   def generate_code(): -> Option D

4. Quick Check:

   Python function = def name(): [OK]

Hint: Python functions start with def and parentheses [OK]

Common Mistakes:

• Using JavaScript function keyword in Python
• Missing parentheses after function name
• Using brackets instead of parentheses

3. What will be the output of this Python code generated by AI?

def add_numbers(a, b):
    return a + b

result = add_numbers(3, 4)
print(result)

medium

A. 7

B. 34

C. TypeError

D. None

Solution

1. Step 1: Understand function behavior

   The function adds two numbers and returns the sum.

2. Step 2: Calculate add_numbers(3, 4)

   3 + 4 equals 7, so result is 7 and printed.

3. Final Answer:

   7 -> Option A

4. Quick Check:

   3 + 4 = 7 [OK]

Hint: Adding numbers returns their sum [OK]

Common Mistakes:

• Thinking + concatenates numbers as strings
• Expecting error from simple addition
• Confusing return value with print output

4. Identify the error in this AI-generated Python code:

def multiply(x, y):
return x * y

print(multiply(2, 3))

medium

A. Missing indentation for return statement

B. Wrong function name

C. Missing parentheses in print

D. Using * instead of + operator

Solution

1. Step 1: Check Python indentation rules

   Python requires the return line inside function to be indented.

2. Step 2: Identify error in code

   Return is not indented, causing IndentationError; other options are incorrect.

3. Final Answer:

   Missing indentation for return statement -> Option A

4. Quick Check:

   Python needs indented blocks [OK]

Hint: Indent inside functions in Python [OK]

Common Mistakes:

• Ignoring indentation errors
• Thinking print needs no parentheses in Python 3
• Confusing operators without context

5. You want to generate Python code that creates a dictionary from a list of keys ["a", "b", "c"] with values as their lengths. Which code snippet correctly uses dictionary comprehension?

hard

A. result = {len(k): k for k in ["a", "b", "c"]}

B. result = [k: len(k) for k in ["a", "b", "c"]]

C. result = {k: len(k) for k in ["a", "b", "c"]}

D. result = {k, len(k) for k in ["a", "b", "c"]}

Solution

1. Step 1: Understand dictionary comprehension syntax

   It uses curly braces with key:value pairs inside a for loop.

2. Step 2: Check each option

   result = {k: len(k) for k in ["a", "b", "c"]} correctly creates dict with keys and their lengths; B uses list brackets wrongly; C swaps key and value; D uses comma instead of colon.

3. Final Answer:

   result = {k: len(k) for k in ["a", "b", "c"]} -> Option C

4. Quick Check:

   Dict comprehension = {key: value for item} [OK]

Hint: Dict comprehension uses {key: value for item} [OK]

Common Mistakes:

• Using list brackets [] instead of {}
• Swapping keys and values
• Using comma instead of colon in dict