Code generation in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is code generation in AI?

Code generation is when AI creates computer code automatically based on instructions or examples given by a user.

beginner

Name a common use case for AI code generation.

AI code generation is often used to help programmers write code faster, fix bugs, or create code snippets from natural language descriptions.

intermediate

How does AI understand what code to generate?

AI models learn patterns from lots of example code and text, so when given a prompt, they predict the next parts of code that fit best.

intermediate

What is a key challenge in AI code generation?

A key challenge is making sure the generated code is correct, safe, and does what the user wants without errors or security risks.

beginner

Give an example of a prompt you might give to an AI code generator.

Example prompt: "Write a Python function that adds two numbers and returns the result."

What does AI code generation primarily rely on?

A Learning from large amounts of code examples

B Randomly guessing code lines

C Manually writing code by humans

D Using only mathematical formulas

AI code generation works by learning patterns from many code examples to predict new code.

Which of these is NOT a typical use of AI code generation?

A Writing code snippets from descriptions

B Fixing bugs automatically

C Replacing all human programmers immediately

D Suggesting improvements to code

AI helps programmers but does not fully replace them yet.

What is a common input for AI code generation?

A Audio recordings

B Natural language instructions

C Hand-drawn sketches

D Mathematical equations only

AI code generators often take natural language prompts to create code.

Why is verifying AI-generated code important?

A To ensure it works correctly and safely

B Because AI code is always perfect

C To make it run slower

D To confuse users

Checking AI code helps avoid errors and security problems.

Which programming language is commonly used in AI code generation examples?

A Assembly language only

B Latin

C HTML only

D Python

Python is popular for AI and often used in code generation.

Explain in your own words what AI code generation is and how it helps programmers.

Think about how AI can turn words into code.

List some challenges or risks when using AI to generate code.

Consider what might go wrong if AI code is not checked.

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