Contextual compression in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the main goal of contextual compression in AI?

Contextual compression is used in AI to reduce the size of input data while preserving important information. What is the primary goal of this technique?

A To remove all redundant information and keep only the contextually relevant parts

B To increase the size of data for better model training

C To randomly select parts of data without considering their importance

D To convert data into a completely different format without preserving meaning

💡 Hint

Think about how compression should keep what matters most for understanding.

Contextual compression aims to keep only the parts of data that are important for the current context, removing redundant or irrelevant information to make processing efficient.

❓ Predict Output

intermediate

Output of simple contextual compression code snippet

Given the following Python code that simulates a simple contextual compression by filtering words based on a context list, what is the output?

Prompt Engineering / GenAI

text = 'The quick brown fox jumps over the lazy dog'
context_words = {'quick', 'fox', 'dog'}
compressed = ' '.join([word for word in text.split() if word in context_words])
print(compressed)

A "The quick brown fox jumps over the lazy dog"

B "quick brown fox dog"

C "The brown jumps over the lazy"

D "quick fox dog"

💡 Hint

Look at which words are kept based on the context_words set.

The code keeps only words that are in the context_words set. Those are 'quick', 'fox', and 'dog', joined by spaces.

❓ Model Choice

advanced

Choosing the best model for contextual compression in text data

You want to compress large text data by keeping only contextually important information for a chatbot. Which model type is best suited for this task?

A A transformer-based model with attention mechanisms

B A simple linear regression model

C A k-means clustering model

D A decision tree classifier

💡 Hint

Think about models that understand context and relationships in sequences.

Transformer models with attention can focus on important parts of text, making them ideal for contextual compression.

❓ Metrics

advanced

Evaluating contextual compression quality

Which metric best measures how well a contextual compression method preserves important information while reducing data size?

A Training loss of an unrelated classification model

B Reconstruction accuracy or similarity score between original and decompressed data

C Number of layers in the compression model

D Total time taken to run the compression algorithm

💡 Hint

Consider how to check if important information is still present after compression.

Reconstruction accuracy or similarity scores show how much important information is preserved after compression and decompression.

🔧 Debug

expert

Debugging a contextual compression function that fails to filter correctly

This Python function is supposed to compress text by keeping only words in a given context set. What error or issue does it have?

Prompt Engineering / GenAI

def compress_text(text, context_set):
    return ' '.join(word for word in text if word in context_set)

result = compress_text('hello world', {'hello'})
print(result)

A It raises a TypeError because join expects a list but gets a string

B It raises a SyntaxError due to missing parentheses

C It checks characters instead of words, so it filters letters not words

D It returns an empty string because context_set is empty

💡 Hint

Look at what 'for word in text' iterates over in Python strings.

Iterating over a string yields characters, not words. So the function filters letters, not words, causing incorrect compression.

Practice

1. What is the main goal of contextual compression in AI?

easy

A. Keep only the most important information to save space and time

B. Increase the size of the data for better accuracy

C. Remove all data except the first sentence

D. Add random noise to the data to improve learning

Solution

1. Step 1: Understand the purpose of contextual compression

   Contextual compression aims to reduce data size by keeping only key information.

2. Step 2: Compare options with this purpose

   Only Keep only the most important information to save space and time matches this goal by saving space and time through important info retention.

3. Final Answer:

   Keep only the most important information to save space and time -> Option A

4. Quick Check:

   Contextual compression = Keep important info [OK]

Hint: Remember: compression means keeping key info, not deleting all [OK]

Common Mistakes:

• Thinking compression means deleting everything
• Confusing compression with data expansion
• Assuming random data removal improves results

2. Which of the following is the correct way to describe a simple contextual compression method?

easy

A. Remove all punctuation from the text

B. Select key sentences and remove less useful details

C. Translate text into another language

D. Add extra words to make text longer

Solution

1. Step 1: Identify what simple contextual compression does

   It selects important parts and removes less useful details to reduce size.

2. Step 2: Match options to this description

   Select key sentences and remove less useful details correctly describes selecting key sentences and removing less useful details.

3. Final Answer:

   Select key sentences and remove less useful details -> Option B

4. Quick Check:

   Simple compression = select key parts [OK]

Hint: Focus on keeping key parts, not random removal [OK]

Common Mistakes:

• Confusing compression with translation
• Thinking punctuation removal equals compression
• Adding words instead of removing

3. Given the following text: 'The cat sat on the mat. It was sunny outside. The dog barked loudly.' Which compressed version best shows contextual compression?

medium

A. 'It was sunny outside. The dog barked loudly.'

B. 'The dog barked loudly.'

C. 'The cat sat on the mat. It was sunny outside. The dog barked loudly.'

D. 'The cat sat on the mat. The dog barked loudly.'

Solution

1. Step 1: Identify key information in the text

   The cat sitting and the dog barking are key events; the weather is less important.

2. Step 2: Choose the option that keeps key info and removes less useful details

   'The cat sat on the mat. The dog barked loudly.' keeps the cat and dog events, removing the less important weather sentence.

3. Final Answer:

   'The cat sat on the mat. The dog barked loudly.' -> Option D

4. Quick Check:

   Keep key events, drop less useful info = 'The cat sat on the mat. The dog barked loudly.' [OK]

Hint: Keep main events, drop side details [OK]

Common Mistakes:

• Keeping all sentences without compression
• Removing too much and losing key info
• Choosing only one sentence when more is needed

4. You have a compression function that removes all sentences containing the word 'not'. The input is: 'I do not like rain. The sun is bright. It is not cold.' What is the output?

medium

A. '' (empty string)

B. 'I do not like rain. It is not cold.'

C. 'The sun is bright.'

D. 'I do not like rain. The sun is bright. It is not cold.'

Solution

1. Step 1: Identify sentences containing 'not'

   Sentences 1 and 3 contain 'not' and should be removed.

2. Step 2: Remove those sentences and keep the rest

   Only 'The sun is bright.' remains after removal.

3. Final Answer:

   'The sun is bright.' -> Option C

4. Quick Check:

   Remove 'not' sentences = 'The sun is bright.' [OK]

Hint: Remove sentences with 'not' only [OK]

Common Mistakes:

• Keeping sentences with 'not'
• Removing all sentences
• Returning original text unchanged

5. You want to compress a conversation by keeping only sentences with keywords: ['urgent', 'meeting', 'deadline']. Given the conversation: 'We have a meeting tomorrow. The weather is nice. The deadline is next week. Let's grab lunch.' Which compressed output is correct?

hard

A. 'We have a meeting tomorrow. The deadline is next week.'

B. 'The weather is nice. Let's grab lunch.'

C. 'We have a meeting tomorrow. The weather is nice.'

D. 'Let's grab lunch. The deadline is next week.'

Solution

1. Step 1: Identify sentences containing keywords

   Sentences with 'meeting' and 'deadline' are the first and third sentences.

2. Step 2: Keep only those sentences and remove others

   Keep 'We have a meeting tomorrow.' and 'The deadline is next week.'

3. Final Answer:

   'We have a meeting tomorrow. The deadline is next week.' -> Option A

4. Quick Check:

   Keep keyword sentences = 'We have a meeting tomorrow. The deadline is next week.' [OK]

Hint: Keep sentences with keywords only [OK]

Common Mistakes:

• Keeping sentences without keywords
• Removing all sentences
• Mixing unrelated sentences