Hybrid search (semantic + keyword) in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why combine semantic and keyword search in hybrid search?

Hybrid search uses both semantic and keyword search methods. What is the main benefit of combining these two?

A It reduces the time needed to index documents by skipping keyword extraction.

B It improves search accuracy by capturing both exact matches and related meanings.

C It eliminates the need for any text preprocessing before searching.

D It guarantees that only exact keyword matches are returned.

💡 Hint

Think about how semantic search understands meaning and keyword search looks for exact words.

Semantic search finds related meanings, while keyword search finds exact words. Combining both helps find more relevant results.

❓ Predict Output

intermediate

Output of hybrid search scoring function

Given the following Python code snippet for a hybrid search score, what is the printed output?

Prompt Engineering / GenAI

semantic_score = 0.7
keyword_score = 0.5
alpha = 0.6
hybrid_score = alpha * semantic_score + (1 - alpha) * keyword_score
print(round(hybrid_score, 2))

A 0.65

B 0.58

C 0.60

D 0.62

💡 Hint

Calculate weighted average: multiply semantic score by alpha, keyword score by (1 - alpha), then add.

Hybrid score = 0.6*0.7 + 0.4*0.5 = 0.42 + 0.20 = 0.62

❓ Model Choice

advanced

Choosing a model for semantic search in hybrid search

You want to build a hybrid search system combining keyword and semantic search. Which model is best suited for the semantic search part?

A A pretrained transformer-based sentence embedding model like Sentence-BERT

B A simple TF-IDF vectorizer without embeddings

C A rule-based keyword matching system

D A clustering algorithm like K-Means

💡 Hint

Semantic search needs to understand meaning, not just word counts.

Transformer-based models like Sentence-BERT create embeddings that capture sentence meaning, ideal for semantic search.

❓ Hyperparameter

advanced

Effect of alpha in hybrid search scoring

In hybrid search, the combined score is calculated as:
hybrid_score = alpha * semantic_score + (1 - alpha) * keyword_score.
What happens if alpha is set to 1?

A Only semantic search scores are used, ignoring keyword matches.

B The scores are averaged equally between semantic and keyword search.

C Only keyword search scores are used, ignoring semantic matches.

D The hybrid score becomes zero regardless of input scores.

💡 Hint

Consider what multiplying by 1 or 0 does to each score.

When alpha=1, hybrid_score = 1*semantic_score + 0*keyword_score = semantic_score only.

❓ Metrics

expert

Evaluating hybrid search effectiveness

You run a hybrid search system and want to measure how well it balances semantic and keyword search results. Which metric best captures both relevance and ranking quality?

A Root Mean Squared Error (RMSE)

B Precision at K (P@K)

C Normalized Discounted Cumulative Gain (NDCG)

D Mean Reciprocal Rank (MRR)

💡 Hint

Look for a metric that considers position of relevant results and graded relevance.

NDCG measures ranking quality by considering relevance scores and their positions, ideal for hybrid search evaluation.

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