Multi-query retrieval in Prompt Engineering / GenAI - Model Metrics & Evaluation

Metrics & Evaluation - Multi-query retrieval

Which metric matters for Multi-query retrieval and WHY

In multi-query retrieval, the goal is to find relevant items for several queries at once. The key metrics are Recall and Mean Average Precision (MAP). Recall tells us how many relevant items we found out of all possible relevant items, which is important because missing relevant results hurts user experience. MAP measures how well the system ranks relevant items higher, which matters because users usually look at top results first.

Confusion matrix or equivalent visualization

For each query, results can be:

  Relevant (R) and Retrieved (Ret) = True Positive (TP)
  Not Relevant and Retrieved = False Positive (FP)
  Relevant and Not Retrieved = False Negative (FN)
  Not Relevant and Not Retrieved = True Negative (TN)

Example for one query:
TP = 8, FP = 2, FN = 4, TN = 86
Total items = 100
Precision = TP / (TP + FP) = 8 / (8 + 2) = 0.8
Recall = TP / (TP + FN) = 8 / (8 + 4) = 0.67

Precision vs Recall tradeoff with concrete examples

In multi-query retrieval, sometimes retrieving more results increases recall but lowers precision because more irrelevant items appear. For example, a search engine showing many results catches more relevant pages (high recall) but also shows unrelated pages (low precision). If the system shows fewer results, precision improves but recall drops, missing some relevant items.

Choosing the right balance depends on the use case. For a legal document search, high recall is critical to not miss any important documents. For a product search on a shopping site, high precision is better to show only relevant products quickly.

What "good" vs "bad" metric values look like for multi-query retrieval

• Good: Recall above 0.8 means most relevant items are found. MAP above 0.7 means relevant items rank near the top.
• Bad: Recall below 0.5 means many relevant items are missed. MAP below 0.4 means relevant items are buried deep in results.

Good systems balance recall and precision to provide useful, relevant results quickly for all queries.

Metrics pitfalls

• Accuracy paradox: Accuracy can be misleading if relevant items are rare. A system that returns mostly irrelevant items can still have high accuracy.
• Data leakage: If queries or relevant items leak into training, metrics look better but don't reflect real performance.
• Overfitting: High metrics on training queries but poor results on new queries show overfitting.
• Ignoring ranking: Treating retrieval as just relevant or not misses how well relevant items are ranked, which MAP captures.

Self-check question

Your multi-query retrieval model has 98% accuracy but only 12% recall on relevant items. Is it good for production? Why or why not?

Answer: No, it is not good. High accuracy can happen if most items are irrelevant and the model mostly predicts irrelevant. But 12% recall means it finds very few relevant items, which defeats the purpose of retrieval. The model misses most relevant results, so users will be unhappy.

Key Result

Recall and Mean Average Precision are key to measure how well multi-query retrieval finds and ranks relevant items.

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