Re-ranking retrieved results in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why use re-ranking in search results?

Imagine you have a list of search results from a simple keyword match. Why might you want to re-rank these results using a machine learning model?

A To remove all results that contain stop words

B To reduce the number of results shown to zero

C To randomly shuffle results for variety

D To improve the order by considering relevance beyond keyword matches

💡 Hint

Think about how simple keyword matching might miss the best answers.

Re-ranking helps by using a model to score results based on deeper relevance, improving the order users see.

❓ Predict Output

intermediate

Output of re-ranking scores

What is the output of the following Python code that re-ranks a list of documents by their scores?

Prompt Engineering / GenAI

docs = ['doc1', 'doc2', 'doc3']
scores = [0.3, 0.9, 0.5]
ranked_docs = [doc for _, doc in sorted(zip(scores, docs), reverse=True)]
print(ranked_docs)

A ['doc1', 'doc3', 'doc2']

B ['doc2', 'doc3', 'doc1']

C ['doc3', 'doc2', 'doc1']

D ['doc1', 'doc2', 'doc3']

💡 Hint

Look at how sorting with reverse=True orders scores from highest to lowest.

The code sorts documents by their scores descending, so the highest score doc2 comes first.

❓ Model Choice

advanced

Best model type for re-ranking

You want to re-rank search results by understanding the meaning of queries and documents. Which model type is best suited for this?

A A pretrained transformer-based language model fine-tuned for ranking

B A simple linear regression model

C A k-means clustering model

D A decision tree classifier without text features

💡 Hint

Think about models that understand language context deeply.

Transformer models like BERT capture semantic meaning and are effective for re-ranking tasks.

❓ Hyperparameter

advanced

Choosing hyperparameters for re-ranking model training

When training a neural re-ranking model, which hyperparameter setting is most important to prevent overfitting on a small dataset?

A Use a very high learning rate like 1.0

B Use batch size of 1 with no shuffling

C Use dropout regularization with rate around 0.3 to 0.5

D Use zero epochs to avoid training

💡 Hint

Regularization helps models generalize better on small data.

Dropout randomly disables neurons during training, reducing overfitting risk.

❓ Metrics

expert

Evaluating re-ranking effectiveness

You have two re-ranking models. Model A has a Mean Reciprocal Rank (MRR) of 0.75, Model B has an MRR of 0.65. What does this tell you?

A Model A generally ranks the correct answer higher than Model B

B Model B is better because lower MRR means better ranking

C Both models perform the same because MRR is not useful here

D Model B has fewer results to rank, so MRR is not comparable

💡 Hint

MRR measures how high the first correct answer appears on average.

A higher MRR means the model tends to place the correct answer closer to the top of the list.

Practice

1.

What is the main purpose of re-ranking retrieved results in a search system?

easy

A. To sort the initial search results again using a better scoring method

B. To remove duplicate results from the search output

C. To speed up the initial search query processing

D. To translate results into different languages

Solution

1. Step 1: Understand the role of re-ranking

   Re-ranking means sorting results again after the first search to improve order.

2. Step 2: Identify the goal of re-ranking

   The goal is to use a smarter scoring method to show the most relevant results at the top.

3. Final Answer:

   To sort the initial search results again using a better scoring method -> Option A

4. Quick Check:

   Re-ranking = better sorting [OK]

Hint: Re-ranking means sorting results again for better relevance [OK]

Common Mistakes:

• Confusing re-ranking with removing duplicates
• Thinking re-ranking speeds up initial search
• Assuming re-ranking translates results

2.

Which of the following code snippets correctly represents a simple re-ranking step that sorts a list of results by their score in descending order?

results = [{'id': 1, 'score': 0.5}, {'id': 2, 'score': 0.9}, {'id': 3, 'score': 0.7}]
# Re-rank results here

easy

A. results.sort(reverse=True)

B. results.sort(key=lambda x: x['id'])

C. results.sort(key=lambda x: x['score'])

D. results.sort(key=lambda x: x['score'], reverse=True)

Solution

1. Step 1: Identify sorting by score descending

   We want to sort by 'score' in descending order, so reverse=True is needed.

2. Step 2: Check each option

   results.sort(key=lambda x: x['score'], reverse=True) sorts by 'score' with reverse=True, which is correct. Others either sort by 'id' or ascending score or missing key.

3. Final Answer:

   results.sort(key=lambda x: x['score'], reverse=True) -> Option D

4. Quick Check:

   Sort by score descending = results.sort(key=lambda x: x['score'], reverse=True) [OK]

Hint: Sort with key and reverse=True for descending order [OK]

Common Mistakes:

• Forgetting reverse=True for descending sort
• Sorting by wrong key like 'id'
• Using sort without key causing error

3.

Given the following code that re-ranks search results by a new score, what will be the output after re-ranking?

results = [
  {'id': 'a', 'score': 0.3},
  {'id': 'b', 'score': 0.8},
  {'id': 'c', 'score': 0.5}
]

# New scores from a re-ranker
new_scores = {'a': 0.9, 'b': 0.4, 'c': 0.7}

for r in results:
    r['score'] = new_scores[r['id']]

results.sort(key=lambda x: x['score'], reverse=True)
print([r['id'] for r in results])

medium

A. ['b', 'c', 'a']

B. ['a', 'c', 'b']

C. ['c', 'a', 'b']

D. ['a', 'b', 'c']

Solution

1. Step 1: Update scores with new_scores

   Results get scores: 'a' = 0.9, 'b' = 0.4, 'c' = 0.7.

2. Step 2: Sort results by updated score descending

   Sorted order by score: 0.9 ('a'), 0.7 ('c'), 0.4 ('b').

3. Final Answer:

   ['a', 'c', 'b'] -> Option B

4. Quick Check:

   Sort by new scores descending = ['a', 'c', 'b'] [OK]

Hint: Replace scores then sort descending by score [OK]

Common Mistakes:

• Sorting by old scores instead of new
• Sorting ascending instead of descending
• Mixing up ids and scores

4.

Identify the error in this re-ranking code snippet and select the fix:

results = [{'id': 1, 'score': 0.2}, {'id': 2, 'score': 0.5}]
new_scores = {1: 0.7, 2: 0.9}

for r in results:
    r['score'] = new_scores[r['id']]

results.sort(key=lambda x: x['score'], reverse=True)
print(results)

medium

A. Use sorted() instead of sort() to avoid in-place sorting

B. Change new_scores keys to strings to match 'id' type

C. No error; code runs correctly and sorts results

D. Add a try-except block to handle missing keys

Solution

1. Step 1: Check key types in new_scores and results

   Both use integer keys for 'id', so lookup works correctly.

2. Step 2: Verify sorting and printing

   Sorting by updated 'score' descending is valid and prints sorted list.

3. Final Answer:

   No error; code runs correctly and sorts results -> Option C

4. Quick Check:

   Matching key types = no error [OK]

Hint: Check key types match for dictionary lookups [OK]

Common Mistakes:

• Assuming string keys when they are integers
• Thinking sort() causes error without reason
• Adding unnecessary try-except blocks

5.

You have a list of 5 retrieved documents with initial scores. You want to re-rank them using a machine learning model that outputs a relevance score. Which approach best improves the final ranking?

1. Use the model scores to replace initial scores and sort descending.
2. Combine initial and model scores by averaging, then sort descending.
3. Sort only by initial scores, ignoring model scores.
4. Randomly shuffle results to avoid bias.

hard

A. Combine initial and model scores by averaging, then sort descending

B. Use the model scores to replace initial scores and sort descending

C. Sort only by initial scores, ignoring model scores

D. Randomly shuffle results to avoid bias

Solution

1. Step 1: Understand re-ranking with model scores

   Replacing scores fully may ignore useful initial info; combining scores balances both.

2. Step 2: Evaluate options for best ranking

   Averaging initial and model scores uses all info, improving relevance and stability.

3. Final Answer:

   Combine initial and model scores by averaging, then sort descending -> Option A

4. Quick Check:

   Combine scores for best re-ranking [OK]

Hint: Blend initial and model scores for better ranking [OK]

Common Mistakes:

• Replacing scores blindly losing initial info
• Ignoring model scores completely
• Random shuffling breaks relevance