Data extraction from text in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to extract all email addresses from the text using a regular expression.

Prompt Engineering / GenAI

import re
text = "Contact us at [e-mail] or [e-mail]"
emails = re.findall([1], text)
print(emails)

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

A"[a-z]+"

B"[0-9]+"

C"\d{3}-\d{2}-\d{4}"

D"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}"

Attempts:

3 left

💡 Hint

Common Mistakes

Using a regex that matches only numbers or unrelated patterns.

The regular expression [a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,} matches typical email addresses in the text.

2fill in blank

medium

Complete the code to extract all dates in the format YYYY-MM-DD from the text.

Prompt Engineering / GenAI

import re
text = "The event is on 2024-06-15 and registration ends 2024-05-30."
dates = re.findall([1], text)
print(dates)

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

A"\d{2}/\d{2}/\d{4}"

B"\d{4}-\d{2}-\d{2}"

C"[a-zA-Z]+ \d{1,2}, \d{4}"

D"\d{4}/\d{2}/\d{2}"

Attempts:

3 left

💡 Hint

Common Mistakes

Using slashes instead of dashes or wrong digit counts.

The regex \d{4}-\d{2}-\d{2} matches dates in the format YYYY-MM-DD.

3fill in blank

hard

Fix the error in the code to extract phone numbers in the format (XXX) XXX-XXXX.

Prompt Engineering / GenAI

import re
text = "Call me at [phone] or [phone]."
phones = re.findall([1], text)
print(phones)

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

A"\(\d{3}\) \d{3}-\d{4}"

B"\d{3}-\d{3}-\d{4}"

C"\(\d{3}\)\d{3}-\d{4}"

D"\d{10}"

Attempts:

3 left

💡 Hint

Common Mistakes

Not escaping parentheses or missing the space after them.

The regex \(\d{3}\) \d{3}-\d{4} correctly matches phone numbers with parentheses and space.

4fill in blank

hard

Fill both blanks to create a dictionary of words and their lengths from the text, including only words longer than 4 letters.

Prompt Engineering / GenAI

text = "Machine learning extracts useful data from text"
words = text.split()
lengths = { [1] : [2] for word in words if len(word) > 4 }
print(lengths)

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

Aword

Blen(word)

Cword.upper()

Dlen(text)

Attempts:

3 left

💡 Hint

Common Mistakes

Using the whole text length or uppercase words as keys or values.

The dictionary comprehension uses each word as key and its length len(word) as value, filtering words longer than 4 letters.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words and their lengths, including only words with length greater than 3.

Prompt Engineering / GenAI

text = "Data extraction from text is useful"
words = text.split()
result = { [1] : [2] for word in words if [3] }
print(result)

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

Aword.upper()

Blen(word)

Clen(word) > 3

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase words as keys or wrong filter conditions.

The dictionary comprehension uses the uppercase version of each word as key, its length as value, and filters words longer than 3 letters.

Practice

1. What is the main goal of data extraction from text in AI?

easy

A. To find and pull out useful information like names and dates from text

B. To translate text from one language to another

C. To generate new text based on a prompt

D. To compress text files to save space

Solution

1. Step 1: Understand the purpose of data extraction

   Data extraction means finding specific useful info inside text, such as names, dates, or places.

2. Step 2: Compare options to the definition

   Only To find and pull out useful information like names and dates from text matches this purpose exactly, while others describe different tasks like translation or compression.

3. Final Answer:

   To find and pull out useful information like names and dates from text -> Option A

4. Quick Check:

   Data extraction = find useful info [OK]

Hint: Look for the option about finding info inside text [OK]

Common Mistakes:

• Confusing extraction with translation
• Thinking extraction means generating new text
• Mixing extraction with file compression

2. Which of the following is the correct way to call a function extract_entities with a text input doc in Python?

easy

A. extract_entities = doc()

B. extract_entities(doc)

C. extract_entities.doc()

D. extract_entities->doc()

Solution

1. Step 1: Recall Python function call syntax

   In Python, to call a function with an argument, use function_name(argument).

2. Step 2: Check each option

   extract_entities(doc) uses correct syntax: extract_entities(doc). Options A, C, and D are invalid Python syntax for calling a function.

3. Final Answer:

   extract_entities(doc) -> Option B

4. Quick Check:

   Function call = function_name(argument) [OK]

Hint: Remember Python calls use parentheses with arguments inside [OK]

Common Mistakes:

• Using dot notation to call a function
• Assigning function call to function name
• Using arrow notation like other languages

3. Given this Python code using a simple extraction model:

text = "Alice met Bob on 2023-04-01 in Paris."
entities = extract_entities(text)
print(entities)

If extract_entities returns a list of tuples with (entity, type), what is the expected output?

medium

A. {'Alice': 'PERSON', 'Bob': 'PERSON', '2023-04-01': 'DATE', 'Paris': 'LOCATION'}

B. ['Alice', 'Bob', '2023-04-01', 'Paris']

C. None

D. [('Alice', 'PERSON'), ('Bob', 'PERSON'), ('2023-04-01', 'DATE'), ('Paris', 'LOCATION')]

Solution

1. Step 1: Understand the function output format

   The function returns a list of tuples, each tuple has (entity, type).

2. Step 2: Match output to expected format

   [('Alice', 'PERSON'), ('Bob', 'PERSON'), ('2023-04-01', 'DATE'), ('Paris', 'LOCATION')] matches a list of tuples with entity and type pairs. ['Alice', 'Bob', '2023-04-01', 'Paris'] is just a list of strings, A is a dictionary, and D is None.

3. Final Answer:

   [('Alice', 'PERSON'), ('Bob', 'PERSON'), ('2023-04-01', 'DATE'), ('Paris', 'LOCATION')] -> Option D

4. Quick Check:

   List of (entity, type) tuples = [('Alice', 'PERSON'), ('Bob', 'PERSON'), ('2023-04-01', 'DATE'), ('Paris', 'LOCATION')] [OK]

Hint: Look for list of tuples format with entity and type [OK]

Common Mistakes:

• Confusing list of strings with list of tuples
• Expecting dictionary instead of list
• Assuming function returns None

4. You have this code snippet:

def extract_entities(text):
    entities = []
    for word in text.split():
        if word.istitle():
            entities.append((word, 'PERSON'))
    return entities

text = "John and Mary went to London."
print(extract_entities(text))

What is the bug in this code for extracting entities?

medium

A. It only detects words starting with uppercase, missing multi-word names

B. It does not split text into words

C. It returns a string instead of a list

D. It crashes because of missing import

Solution

1. Step 1: Analyze the extraction logic

   The code checks if each word starts with uppercase (istitle) and labels it as 'PERSON'.

2. Step 2: Identify limitation

   This misses multi-word names like 'New York' or full names with multiple words. It only detects single capitalized words.

3. Final Answer:

   It only detects words starting with uppercase, missing multi-word names -> Option A

4. Quick Check:

   Single-word detection limitation = It only detects words starting with uppercase, missing multi-word names [OK]

Hint: Check if code handles multi-word names or just single words [OK]

Common Mistakes:

• Thinking split() is missing
• Assuming return type is wrong
• Expecting import needed for this code

5. You want to extract dates and locations from a large text using a pretrained AI model. Which approach best improves accuracy and speed?

hard

A. Use a generic language model without any fine-tuning

B. Manually write rules to find dates and locations using string matching

C. Use a named entity recognition (NER) model fine-tuned on your domain data

D. Extract all capitalized words as locations and all numbers as dates

Solution

1. Step 1: Consider model choice for extraction

   Fine-tuning a NER model on your specific domain helps it learn patterns and improves accuracy.

2. Step 2: Compare other options

   Manual rules are slow and brittle, generic models lack domain knowledge, and simple heuristics miss many cases.

3. Final Answer:

   Use a named entity recognition (NER) model fine-tuned on your domain data -> Option C

4. Quick Check:

   Fine-tuned NER model = best accuracy and speed [OK]

Hint: Fine-tune NER models for best extraction results [OK]

Common Mistakes:

• Relying on manual rules only
• Using generic models without tuning
• Using simple heuristics that miss cases