Code generation in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to generate text using a simple language model.

Prompt Engineering / GenAI

output = model.generate([1])

Drag options to blanks, or click blank then click option'

Ainput_text

Bmax_length=50

Ctemperature=0.7

Dnum_return_sequences=1

Attempts:

3 left

💡 Hint

Common Mistakes

Passing input_text directly without specifying max_length causes an error.

Using temperature or num_return_sequences alone does not define output length.

The generate function requires the max_length parameter to specify how many tokens to generate.

2fill in blank

medium

Complete the code to tokenize input text before generation.

Prompt Engineering / GenAI

inputs = tokenizer([1], return_tensors='pt')

Drag options to blanks, or click blank then click option'

Ainput_text

Bmax_length=50

Ctemperature=0.7

Dpadding=True

Attempts:

3 left

💡 Hint

Common Mistakes

Passing max_length or temperature instead of the text string.

Forgetting to pass the input text causes an error.

The tokenizer needs the actual input text string to convert it into tokens.

3fill in blank

hard

Fix the error in the code to decode generated tokens correctly.

Prompt Engineering / GenAI

generated_text = tokenizer.decode([1], skip_special_tokens=True)

Drag options to blanks, or click blank then click option'

Aoutput

Binputs

Cinput_text

Dgenerated_ids

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to decode the input tokens or raw output object instead of generated token IDs.

Passing the wrong variable causes a type error.

The decode function needs the generated token IDs, which are stored in generated_ids.

4fill in blank

hard

Fill both blanks to generate text with temperature and return multiple sequences.

Prompt Engineering / GenAI

outputs = model.generate(inputs.input_ids, [1], [2])

Drag options to blanks, or click blank then click option'

Atemperature=0.9

Bmax_length=100

Cnum_return_sequences=3

Ddo_sample=True

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting to set do_sample=True disables temperature effect.

Not setting num_return_sequences returns only one sequence.

do_sample=True enables sampling with temperature, and num_return_sequences=3 returns multiple outputs.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension filtering tokens by length and decoding.

Prompt Engineering / GenAI

result = {token: tokenizer.decode(token) for token in outputs if len(token) [1] [2] and token != [3]

Drag options to blanks, or click blank then click option'

A>

B5

C0

D''

Attempts:

3 left

💡 Hint

Common Mistakes

Using < or <= instead of > changes the filter logic.

Comparing token to 0 instead of empty string causes errors.

We filter tokens longer than 5 and exclude empty strings ('').

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