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Hybrid search (semantic + keyword) in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

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beginner
What is hybrid search in the context of information retrieval?
Hybrid search combines semantic search and keyword search to improve finding relevant information by understanding meaning and matching exact words.
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beginner
How does semantic search differ from keyword search?
Semantic search understands the meaning behind words and finds related concepts, while keyword search looks for exact word matches.
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intermediate
Why combine semantic and keyword search in hybrid search?
Combining both helps find results that match exact terms and also those that are conceptually related, improving accuracy and recall.
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intermediate
What role do embeddings play in semantic search?
Embeddings convert words or sentences into numbers that capture their meaning, allowing semantic search to compare concepts effectively.
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advanced
Name one challenge when implementing hybrid search.
Balancing the weight between semantic similarity and keyword matching to get the best search results can be challenging.
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What does hybrid search combine?
AOnly keyword search
BImage search and video search
CSemantic search and keyword search
DOnly semantic search
Which search type understands the meaning behind words?
AKeyword search
BSemantic search
CBoolean search
DExact match search
What is a key benefit of hybrid search?
ABetter balance of exact and related results
BFaster indexing
COnly finds exact word matches
DRemoves all irrelevant results
What technology helps semantic search compare meanings?
AInverted index
BRegular expressions
CStop words
DEmbeddings
What is a challenge in hybrid search?
ABalancing semantic and keyword scores
BNot indexing data
CUsing only semantic search
DIgnoring keyword matches
Explain how hybrid search improves search results compared to using only keyword or semantic search.
Think about how understanding meaning and exact words together helps find better answers.
You got /3 concepts.
    Describe the role of embeddings in semantic search within a hybrid search system.
    Consider how computers understand text meaning using numbers.
    You got /3 concepts.

      Practice

      (1/5)
      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