Hybrid search strategies in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Hybrid Search Strategy Components

Which two main components are combined in a hybrid search strategy to improve search results?

A Decision trees and random forests

B Vector similarity search and keyword-based search

C Clustering and dimensionality reduction

D Reinforcement learning and supervised learning

💡 Hint

Think about combining semantic understanding with exact matching.

Hybrid search strategies combine vector similarity search, which captures semantic meaning, with keyword-based search, which matches exact words, to improve search accuracy and relevance.

❓ Model Choice

intermediate

Choosing a Model for Vector Search in Hybrid Systems

Which model type is best suited to generate embeddings for vector similarity search in a hybrid search system?

A Transformer-based language model trained on text

B Convolutional Neural Network (CNN) trained on images

C Linear regression model

D K-means clustering model

💡 Hint

Consider which model understands text semantics well.

Transformer-based language models like BERT or GPT generate meaningful text embeddings that capture semantic relationships, making them ideal for vector similarity search.

❓ Metrics

advanced

Evaluating Hybrid Search Performance

Which metric best measures the balance between precision and recall in hybrid search results?

A Mean Squared Error (MSE)

B Log Loss

C F1 Score

D Accuracy

💡 Hint

This metric combines precision and recall into one number.

F1 Score is the harmonic mean of precision and recall, making it suitable to evaluate how well hybrid search balances finding relevant results and avoiding irrelevant ones.

🔧 Debug

advanced

Debugging Hybrid Search Code Output

What is the output of this Python code snippet simulating a hybrid search result ranking? ```python vector_scores = [0.9, 0.75, 0.6] keyword_scores = [0.8, 0.85, 0.5] combined_scores = [v * 0.6 + k * 0.4 for v, k in zip(vector_scores, keyword_scores)] sorted_indices = sorted(range(len(combined_scores)), key=lambda i: combined_scores[i], reverse=True) print(sorted_indices) ```

A [0, 1, 2]

B [1, 0, 2]

C [2, 1, 0]

D [0, 2, 1]

💡 Hint

Calculate combined scores and sort descending.

Combined scores are [0.9*0.6+0.8*0.4=0.54+0.32=0.86, 0.75*0.6+0.85*0.4=0.45+0.34=0.79, 0.6*0.6+0.5*0.4=0.36+0.2=0.56]. Sorted descending indices are [0,1,2].

❓ Hyperparameter

expert

Tuning Weight Parameters in Hybrid Search

In a hybrid search system combining vector and keyword scores as `final_score = alpha * vector_score + (1 - alpha) * keyword_score`, which alpha value is most likely to emphasize semantic similarity over exact keyword matches?

A 0.0

B 0.5

C 0.1

D 0.9

💡 Hint

Higher alpha means more weight on vector scores.

Alpha close to 1 (like 0.9) gives more weight to vector similarity, emphasizing semantic meaning over keyword matching.

Practice

1.

What is the main benefit of using a hybrid search strategy in AI?

easy

A. It relies solely on embedding similarity for accuracy.

B. It uses only keyword matching for faster results.

C. It combines different search methods to improve results.

D. It avoids using any search algorithms.

Solution

1. Step 1: Understand hybrid search purpose

   Hybrid search mixes different search methods to get better results than using one method alone.

2. Step 2: Compare options

   It combines different search methods to improve results. correctly states the benefit. The other options either describe single-method approaches or are incorrect.

3. Final Answer:

   It combines different search methods to improve results. -> Option C

4. Quick Check:

   Hybrid search = mix methods [OK]

Hint: Hybrid means mixing methods for better results [OK]

Common Mistakes:

• Thinking hybrid means using only one search method
• Confusing hybrid search with keyword-only search
• Ignoring the benefit of combining methods

2.

Which of the following is the correct way to combine keyword and embedding search scores in a hybrid search?

final_score = ?

easy

A. final_score = 0.5 * keyword_score + 0.5 * embedding_score

B. final_score = keyword_score * embedding_score

C. final_score = max(keyword_score, embedding_score)

D. final_score = keyword_score - embedding_score

Solution

1. Step 1: Understand score combination

   Hybrid search often combines scores by weighted sum to balance keyword and embedding contributions.

2. Step 2: Evaluate options

   final_score = 0.5 * keyword_score + 0.5 * embedding_score uses weighted sum, which is common. Multiplying scores can distort results. Taking the max ignores combined info. Subtracting can give negative scores.

3. Final Answer:

   final_score = 0.5 * keyword_score + 0.5 * embedding_score -> Option A

4. Quick Check:

   Weighted sum combines scores [OK]

Hint: Use weighted sum to combine scores in hybrid search [OK]

Common Mistakes:

• Multiplying scores causing skewed results
• Using max ignores combined info
• Subtracting scores can produce negatives

3.

Given the following Python code snippet for hybrid search scoring, what is the output?

keyword_scores = [0.8, 0.6, 0.9]
embedding_scores = [0.7, 0.9, 0.5]
final_scores = [0.5 * k + 0.5 * e for k, e in zip(keyword_scores, embedding_scores)]
print(final_scores)

medium

A. [0.8, 0.9, 0.5]

B. [0.75, 0.75, 0.7]

C. [0.56, 0.54, 0.7]

D. [1.5, 1.5, 1.4]

Solution

1. Step 1: Calculate each final score

   For each pair: (0.8+0.7)/2=0.75, (0.6+0.9)/2=0.75, (0.9+0.5)/2=0.7

2. Step 2: Verify output list

   The list is [0.75, 0.75, 0.7], matching [0.75, 0.75, 0.7].

3. Final Answer:

   [0.75, 0.75, 0.7] -> Option B

4. Quick Check:

   Average scores = [0.75, 0.75, 0.7] [OK]

Hint: Average keyword and embedding scores for final score [OK]

Common Mistakes:

• Adding scores without dividing by 2
• Mixing order of scores
• Printing original scores instead of combined

4.

Identify the error in this hybrid search score calculation code and select the fix:

keyword_scores = [0.9, 0.7]
embedding_scores = [0.6]
final_scores = [0.5 * k + 0.5 * e for k, e in zip(keyword_scores, embedding_scores)]
print(final_scores)

medium

A. No error; code runs fine.

B. Use '+' instead of '*' in score calculation.

C. Replace zip with map to fix length mismatch.

D. Lists have different lengths; use min length or pad shorter list.

Solution

1. Step 1: Check list lengths

   keyword_scores has 2 elements, embedding_scores has 1 element, causing zip to truncate to 1 element.

2. Step 2: Fix length mismatch

   Lists have different lengths; use min length or pad shorter list. suggests using min length or padding shorter list to avoid losing data.

3. Final Answer:

   Lists have different lengths; use min length or pad shorter list. -> Option D

4. Quick Check:

   Length mismatch needs handling [OK]

Hint: Check list lengths before zipping in hybrid search [OK]

Common Mistakes:

• Ignoring length mismatch causing data loss
• Changing operators incorrectly
• Assuming zip auto-fills missing values

5.

You want to build a hybrid search system that first filters documents by keywords, then reranks them by embedding similarity. Which approach best fits this goal?

hard

A. Filter documents by keywords, then rerank filtered set by embedding similarity.

B. Run embedding search first, then filter results by keywords.

C. Combine keyword and embedding scores equally on all documents without filtering.

D. Use only keyword search for filtering and ignore embeddings.

Solution

1. Step 1: Understand filtering and reranking

   Filtering by keywords narrows down documents quickly; reranking by embeddings improves relevance.

2. Step 2: Match approach to goal

   Filter documents by keywords, then rerank filtered set by embedding similarity. matches the goal: filter first, then rerank. Run embedding search first, then filter results by keywords. reverses order, less efficient. Combine keyword and embedding scores equally on all documents without filtering. skips filtering, less efficient. Use only keyword search for filtering and ignore embeddings. ignores embeddings, losing semantic power.

3. Final Answer:

   Filter documents by keywords, then rerank filtered set by embedding similarity. -> Option A

4. Quick Check:

   Filter then rerank = best hybrid approach [OK]

Hint: Filter first, rerank second for efficient hybrid search [OK]

Common Mistakes:

• Reranking before filtering wastes resources
• Ignoring filtering step reduces speed
• Using only one method loses hybrid benefits