Hybrid search strategies in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to combine vector similarity and keyword matching in a hybrid search.

Prompt Engineering / GenAI

hybrid_score = vector_score * [1] + keyword_score

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

A1.0

B0.5

C2.0

Dvector_score

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1.0 or 2.0 makes vector similarity dominate too much.

Using 'vector_score' again causes a wrong calculation.

The vector similarity score is weighted by 0.5 before adding the keyword score to balance both methods.

2fill in blank

medium

Complete the code to filter search results by a minimum hybrid score threshold.

Prompt Engineering / GenAI

filtered_results = [r for r in results if r['hybrid_score'] [1] 0.7]

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

A==

B<

C!=

D>=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' filters out good results.

Using '==' is too strict and excludes most results.

We keep results with hybrid scores greater than or equal to 0.7 to ensure relevance.

3fill in blank

hard

Fix the error in the code that calculates the final hybrid score by normalizing vector and keyword scores.

Prompt Engineering / GenAI

final_score = (vector_score / max_vector) * [1] + (keyword_score / max_keyword)

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

Avector_score

Bmax_keyword

Cweight_vector

Dweight_keyword

Attempts:

3 left

💡 Hint

Common Mistakes

Multiplying by max_keyword mixes up normalization.

Using vector_score again ignores normalization.

The vector score is multiplied by its weight before adding the normalized keyword score.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps document IDs to their hybrid scores only if the score is above 0.6.

Prompt Engineering / GenAI

hybrid_dict = {doc['id']: doc['score'] for doc in docs if doc['score'] [1] 0.6 and doc['id'] [2] None}

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

A>

B!=

C==

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' includes low scores.

Using '==' None excludes valid IDs.

We select docs with scores greater than 0.6 and IDs that are not None.

5fill in blank

hard

Fill all three blanks to create a list comprehension that extracts titles of documents with hybrid scores above 0.75 and keyword matches greater than 2.

Prompt Engineering / GenAI

selected_titles = [doc[1] for doc in documents if doc['hybrid_score'] [2] 0.75 and doc['keyword_matches'] [3] 2]

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

A['title']

B>

D['id']

Attempts:

3 left

💡 Hint

Common Mistakes

Selecting 'id' instead of 'title'.

Using '<' includes unwanted docs.

We extract the 'title' field for docs with hybrid_score > 0.75 and keyword_matches > 2.

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