Hybrid search (semantic + keyword) in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to combine semantic and keyword search scores by adding them.

Prompt Engineering / GenAI

final_score = semantic_score [1] keyword_score

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

A*

B-

C+

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiplication instead of addition

Using subtraction which lowers the score

We add semantic and keyword scores to get a combined relevance score.

2fill in blank

medium

Complete the code to normalize the semantic vector before searching.

Prompt Engineering / GenAI

normalized_vector = semantic_vector [1] np.linalg.norm(semantic_vector)

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

A/

B*

C+

D-

Attempts:

3 left

💡 Hint

Common Mistakes

Multiplying instead of dividing

Adding or subtracting which does not normalize

Dividing by the vector's norm scales it to length 1, which is normalization.

3fill in blank

hard

Fix the error in the code to filter documents containing the keyword 'AI'.

Prompt Engineering / GenAI

filtered_docs = [doc for doc in documents if 'AI' [1] doc['keywords']]

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

A==

Bin

Cnot in

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '==' which compares equality incorrectly

Using 'not in' which excludes documents with 'AI'

The 'in' keyword checks if 'AI' is inside the keywords list of each document.

4fill in blank

hard

Fill both blanks to create a dictionary of document IDs and their combined scores, filtering for scores above 0.5.

Prompt Engineering / GenAI

result = {doc['id']: doc['semantic_score'] [1] doc['keyword_score'] for doc in docs if (doc['semantic_score'] [2] doc['keyword_score']) > 0.5}

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

A+

B-

C*

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiplication or division which changes score meaning

Using subtraction which can lower scores incorrectly

We add semantic and keyword scores to get combined scores and filter those above 0.5.

5fill in blank

hard

Fill all three blanks to create a list of document titles where the combined score is above 0.7.

Prompt Engineering / GenAI

top_titles = [doc[[1]] for doc in docs if (doc[[2]] + doc[[3]]) > 0.7]

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

A'title'

B'semantic_score'

C'keyword_score'

D'id'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'id' instead of 'title' for the first blank

Mixing up score fields or using wrong keys

We select the 'title' field for documents where the sum of 'semantic_score' and 'keyword_score' is greater than 0.7.

Practice

1. What is the main advantage of hybrid search combining semantic and keyword methods?

easy

A. It improves search relevance by using both exact words and meaning.

B. It only uses exact keyword matching for faster results.

C. It ignores word meanings to focus on keyword frequency.

D. It replaces keywords with random words for variety.

Solution

1. Step 1: Understand keyword and semantic search roles

   Keyword search finds exact word matches; semantic search finds meaning matches.

2. Step 2: Combine both for better results

   Hybrid search uses both to improve relevance and user satisfaction.

3. Final Answer:

   It improves search relevance by using both exact words and meaning. -> Option A

4. Quick Check:

   Hybrid search = better relevance [OK]

Hint: Hybrid = exact words + meaning for best results [OK]

Common Mistakes:

• Thinking hybrid search uses only keywords
• Assuming semantic search ignores keywords
• Believing hybrid search slows down search always

2. Which of the following is the correct way to combine semantic and keyword scores in hybrid search?

easy

A. final_score = semantic_score * keyword_score

B. final_score = semantic_score / keyword_score

C. final_score = semantic_score - keyword_score

D. final_score = semantic_score + keyword_score

Solution

1. Step 1: Understand score combination methods

   Adding scores balances contributions from both semantic and keyword parts.

2. Step 2: Choose addition for hybrid scoring

   Adding semantic and keyword scores is common to combine relevance signals.

3. Final Answer:

   final_score = semantic_score + keyword_score -> Option D

4. Quick Check:

   Hybrid score = sum of semantic and keyword [OK]

Hint: Add scores to combine semantic and keyword relevance [OK]

Common Mistakes:

• Multiplying scores causing very small or large values
• Subtracting scores losing positive relevance
• Dividing scores causing errors if denominator is zero

3. Given the code snippet:

semantic_scores = [0.8, 0.5, 0.3]
keyword_scores = [0.6, 0.7, 0.4]
final_scores = [s + k for s, k in zip(semantic_scores, keyword_scores)]
print(final_scores)

What is the output?

medium

A. [1.4, 1.2, 0.7]

B. [0.2, -0.2, -0.1]

C. [0.48, 0.35, 0.12]

D. [1.2, 1.4, 0.7]

Solution

1. Step 1: Add corresponding semantic and keyword scores

   0.8+0.6=1.4, 0.5+0.7=1.2, 0.3+0.4=0.7

2. Step 2: Create list of summed scores

   final_scores = [1.4, 1.2, 0.7]

3. Final Answer:

   [1.4, 1.2, 0.7] -> Option A

4. Quick Check:

   Sum pairs = [1.4, 1.2, 0.7] [OK]

Hint: Add pairs element-wise for final scores [OK]

Common Mistakes:

• Multiplying instead of adding scores
• Mixing order of scores in zip
• Confusing subtraction with addition

4. Identify the error in this hybrid search scoring code:

semantic_scores = [0.9, 0.4, 0.7]
keyword_scores = [0.5, 0.6]
final_scores = [s + k for s, k in zip(semantic_scores, keyword_scores)]
print(final_scores)

medium

A. Adding scores should use multiplication instead.

B. Using zip causes a syntax error here.

C. Lists have different lengths causing missing scores.

D. The print statement is missing parentheses.

Solution

1. Step 1: Check list lengths

   semantic_scores has 3 items; keyword_scores has 2 items.

2. Step 2: Understand zip behavior

   zip stops at shortest list length, so last semantic score is ignored.

3. Final Answer:

   Lists have different lengths causing missing scores. -> Option C

4. Quick Check:

   Unequal list lengths truncate results [OK]

Hint: Ensure lists are same length before zipping [OK]

Common Mistakes:

• Assuming zip pads shorter list automatically
• Thinking zip causes syntax error
• Believing multiplication is required for hybrid scores

5. You want to improve a hybrid search system by weighting semantic similarity twice as much as keyword matching. Which formula correctly applies this?

hard

A. final_score = semantic_score + 2 * keyword_score

B. final_score = 2 * semantic_score + keyword_score

C. final_score = semantic_score * keyword_score * 2

D. final_score = (semantic_score + keyword_score) / 2

Solution

1. Step 1: Identify weighting requirement

   Semantic similarity should count double compared to keyword score.

2. Step 2: Apply weights in formula

   Multiply semantic_score by 2, then add keyword_score.

3. Final Answer:

   final_score = 2 * semantic_score + keyword_score -> Option B

4. Quick Check:

   Semantic weighted double = 2 * semantic + keyword [OK]

Hint: Multiply semantic score by 2 before adding keyword [OK]

Common Mistakes:

• Weighting keyword score instead of semantic
• Multiplying all scores together
• Dividing sum instead of weighting