Hybrid search strategies in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a hybrid search strategy in AI?

A hybrid search strategy combines two or more search methods to find solutions more efficiently, often mixing fast but rough methods with slower but precise ones.

beginner

Why use hybrid search strategies instead of just one search method?

Because combining methods can balance speed and accuracy, helping to find good solutions faster than using a single method alone.

intermediate

Name two common search methods often combined in hybrid search strategies.

Heuristic search (like A*) and local search (like hill climbing) are often combined to explore broadly and then refine solutions.

intermediate

How does a hybrid search strategy improve solution quality?

It uses one method to quickly find promising areas and another to carefully explore those areas, improving the chance of finding better solutions.

beginner

Give an example of a real-life situation where hybrid search strategies could be useful.

Planning a trip: first use a fast method to find possible routes, then a detailed method to pick the best route considering traffic and preferences.

What is the main goal of hybrid search strategies?

A To avoid searching altogether

B To combine strengths of different search methods

C To use only one search method repeatedly

D To slow down the search process

Hybrid search strategies aim to combine the strengths of different methods to improve search efficiency and solution quality.

Which of these is NOT typically part of a hybrid search strategy?

A Heuristic search

B Local search

C Systematic exploration

D Random guessing without refinement

Random guessing without refinement is not effective and usually not part of hybrid search strategies.

Hybrid search strategies help balance which two aspects?

A Speed and accuracy

B Memory and storage

C Input and output

D Hardware and software

They balance speed (fast search) and accuracy (precise search) to find good solutions efficiently.

In hybrid search, what role does a heuristic search usually play?

A Randomly selecting options

B Ignoring the problem constraints

C Quickly finding promising areas

D Slowing down the search

Heuristic search quickly guides the search toward promising areas to explore further.

Which is a benefit of using hybrid search strategies?

A Finding better solutions faster

B Using more computer memory

C Making the search process more confusing

D Avoiding any search at all

Hybrid search strategies help find better solutions faster by combining different search methods.

Explain what hybrid search strategies are and why they are useful.

Think about mixing fast and careful search methods.

Describe a real-life example where hybrid search strategies could help solve a problem.

Consider planning or decision-making tasks.

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