Multi-query retrieval in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the main advantage of using multi-query retrieval in search systems?

Imagine you want to find information on a complex topic that has several aspects. Why would a multi-query retrieval system be better than a single-query system?

A It simplifies the search by ignoring less important parts of the topic.

B It reduces the number of queries needed by combining all keywords into one long query.

C It guarantees faster retrieval by only using one query at a time.

D It allows searching multiple aspects simultaneously, improving recall and relevance.

💡 Hint

Think about how covering different angles of a topic can help find more useful results.

Multi-query retrieval sends several focused queries to cover different aspects of a topic, which helps find more relevant documents and improves recall compared to a single broad query.

❓ Predict Output

intermediate

Output of multi-query retrieval scoring aggregation

Given the following Python code that simulates scores from multiple queries for documents, what is the final aggregated score for document 'doc2'?

Prompt Engineering / GenAI

query_scores = {
    'query1': {'doc1': 0.8, 'doc2': 0.5, 'doc3': 0.3},
    'query2': {'doc1': 0.4, 'doc2': 0.7, 'doc3': 0.6},
    'query3': {'doc1': 0.3, 'doc2': 0.2, 'doc3': 0.9}
}

# Aggregate scores by summing scores from all queries per document
aggregated_scores = {}
for q_scores in query_scores.values():
    for doc, score in q_scores.items():
        aggregated_scores[doc] = aggregated_scores.get(doc, 0) + score

final_score_doc2 = aggregated_scores['doc2']
print(final_score_doc2)

A 0.9

B 1.4

C 1.2

D 1.0

💡 Hint

Sum the scores for 'doc2' from each query.

Scores for 'doc2' are 0.5 (query1) + 0.7 (query2) + 0.2 (query3) = 1.4.

❓ Model Choice

advanced

Best model architecture for multi-query retrieval embeddings

You want to build a system that generates embeddings for multiple queries to retrieve documents effectively. Which model architecture is best suited for encoding multiple queries independently but efficiently?

A A recurrent neural network that concatenates all queries into one sequence before encoding.

B A convolutional neural network that treats all queries as one long image input.

C A single shared transformer encoder that processes each query separately to produce embeddings.

D A decision tree model that classifies queries into categories before retrieval.

💡 Hint

Think about models that can handle sequences independently and produce vector representations.

A shared transformer encoder can efficiently encode each query independently into embeddings, which is ideal for multi-query retrieval systems.

❓ Hyperparameter

advanced

Choosing the best aggregation method hyperparameter for multi-query retrieval

In multi-query retrieval, you combine scores from multiple queries to rank documents. Which hyperparameter choice affects how you weight each query's contribution?

A The query weight vector that assigns importance to each query's score before aggregation.

B The dropout rate applied in the neural network layers.

C The batch size used during model training.

D The learning rate used to train the embedding model.

💡 Hint

Focus on parameters that control score combination rather than model training.

The query weight vector controls how much each query's score influences the final aggregated score, directly affecting retrieval ranking.

❓ Metrics

expert

Evaluating multi-query retrieval with combined metrics

You have retrieval results from multiple queries combined to rank documents. Which evaluation metric best captures both the relevance and ranking quality across all queries?

A Mean Reciprocal Rank (MRR) averaged over all queries.

B Accuracy of classifying documents as relevant or not.

C Mean Squared Error (MSE) between predicted and true scores.

D Confusion matrix counts aggregated over all queries.

💡 Hint

Think about metrics designed for ranking quality in retrieval tasks.

MRR measures the average rank position of the first relevant document per query, making it suitable for multi-query retrieval evaluation.

Practice

1. What is the main advantage of multi-query retrieval in search systems?

easy

A. It deletes irrelevant data automatically

B. It stores data in a smaller space

C. It improves the quality of a single search result

D. It runs many searches at once to get results faster

Solution

1. Step 1: Understand the purpose of multi-query retrieval

   Multi-query retrieval is designed to handle multiple search queries simultaneously.

2. Step 2: Identify the main benefit

   Running many searches at once speeds up getting results compared to running queries one by one.

3. Final Answer:

   It runs many searches at once to get results faster -> Option D

4. Quick Check:

   Multi-query retrieval = faster multiple searches [OK]

Hint: Think: multiple queries done together means faster results [OK]

Common Mistakes:

• Confusing speed with data storage
• Thinking it improves single query quality
• Assuming it deletes data automatically

2. Which of the following is the correct way to represent multiple queries for multi-query retrieval in Python?

easy

A. queries = ['query1', 'query2', 'query3']

B. queries = 'query1, query2, query3'

C. queries = {'query1': 1, 'query2': 2}

D. queries = query1 + query2 + query3

Solution

1. Step 1: Identify the correct data structure for multiple queries

   Multiple queries should be stored as a list of strings to keep them separate.

2. Step 2: Check each option

   queries = ['query1', 'query2', 'query3'] uses a list of strings, which is correct. queries = 'query1, query2, query3' is a single string, not multiple queries. queries = {'query1': 1, 'query2': 2} is a dictionary, which is not standard for query lists. queries = query1 + query2 + query3 tries to add strings, which concatenates them, not separate queries.

3. Final Answer:

   queries = ['query1', 'query2', 'query3'] -> Option A

4. Quick Check:

   List of strings = multiple queries [OK]

Hint: Use a list to hold multiple queries separately [OK]

Common Mistakes:

• Using a single string instead of a list
• Using a dictionary instead of a list
• Concatenating queries into one string

3. Given the following Python code for multi-query retrieval, what will be the output?

queries = ['apple', 'banana']
results = {q: q.upper() for q in queries}
print(results)

medium

A. {'apple': 'APPLE', 'banana': 'BANANA'}

B. ['APPLE', 'BANANA']

C. {'APPLE': 'apple', 'BANANA': 'banana'}

D. Error: invalid syntax

Solution

1. Step 1: Understand the dictionary comprehension

   The code creates a dictionary where each query string is a key, and its uppercase version is the value.

2. Step 2: Evaluate the comprehension for each query

   For 'apple', the pair is 'apple': 'APPLE'; for 'banana', 'banana': 'BANANA'.

3. Final Answer:

   {'apple': 'APPLE', 'banana': 'BANANA'} -> Option A

4. Quick Check:

   Dict comprehension maps keys to uppercase values [OK]

Hint: Dict comprehension maps each query to its uppercase [OK]

Common Mistakes:

• Confusing list output with dict output
• Swapping keys and values
• Thinking code has syntax error

4. Identify the error in this multi-query retrieval code snippet:

queries = ['cat', 'dog']
results = []
for q in queries:
    results.append(q.upper)
print(results)

medium

A. Incorrect variable name 'q' in loop

B. Using list instead of dictionary for results

C. Missing parentheses after upper method call

D. Syntax error in for loop

Solution

1. Step 1: Check method usage in loop

   The code calls q.upper without parentheses, so it references the method but does not call it.

2. Step 2: Understand the effect of missing parentheses

   Appending q.upper adds the method object, not the uppercase string, causing unexpected results.

3. Final Answer:

   Missing parentheses after upper method call -> Option C

4. Quick Check:

   Method call needs () to execute [OK]

Hint: Remember to add () to call string methods like upper() [OK]

Common Mistakes:

• Forgetting parentheses on method calls
• Thinking list is wrong for storing results
• Assuming variable name is incorrect

5. You want to retrieve results for multiple queries from a large dataset efficiently. Which approach best uses multi-query retrieval to improve speed and organize results?

hard

A. Run each query one after another and combine all results into one list

B. Run all queries at once and store each query's results separately in a dictionary

C. Run only the first query and ignore the rest to save time

D. Run queries randomly and merge results without labels

Solution

1. Step 1: Understand multi-query retrieval goal

   It aims to run many queries simultaneously to save time and keep results organized.

2. Step 2: Evaluate options for efficiency and organization

   Run all queries at once and store each query's results separately in a dictionary runs all queries at once and stores results separately, matching the goal. Run each query one after another and combine all results into one list runs queries one by one, slower. Run only the first query and ignore the rest to save time ignores queries, losing data. Run queries randomly and merge results without labels merges results without labels, losing clarity.

3. Final Answer:

   Run all queries at once and store each query's results separately in a dictionary -> Option B

4. Quick Check:

   Simultaneous queries + separate storage = efficient multi-query retrieval [OK]

Hint: Run all queries together and keep results labeled separately [OK]

Common Mistakes:

• Running queries sequentially, losing speed
• Ignoring some queries to save time
• Merging results without query labels