Multi-query retrieval in Prompt Engineering / GenAI - Model Pipeline Trace

Model Pipeline - Multi-query retrieval

Multi-query retrieval is a process where multiple questions or queries are used together to find the best matching information from a large collection of data. It helps improve search results by combining different queries to get more accurate answers.

Data Flow - 6 Stages

1Input Queries

5 queries→Receive multiple queries from user→5 queries

["What is AI?", "Explain machine learning", "Benefits of AI", "AI applications", "Future of AI"]

↓

2Query Embedding

5 queries→Convert each query into a vector representation→5 vectors of size 128

[[0.12, 0.34, ..., 0.56], [0.22, 0.11, ..., 0.44], ...]

↓

3Document Embedding

10000 documents→Convert documents into vector representations→10000 vectors of size 128

[[0.10, 0.30, ..., 0.50], [0.20, 0.10, ..., 0.40], ...]

↓

4Similarity Calculation

5 query vectors, 10000 document vectors→Calculate similarity scores between each query and all documents→5 arrays of 10000 similarity scores

[[0.8, 0.1, ..., 0.3], [0.7, 0.2, ..., 0.4], ...]

↓

5Score Aggregation

5 arrays of 10000 scores→Combine scores from all queries for each document→1 array of 10000 aggregated scores

[0.85, 0.25, ..., 0.45]

↓

6Ranking and Retrieval

1 array of 10000 scores→Sort documents by aggregated score and select top results→Top 10 documents

[DocID 123, DocID 456, ..., DocID 789]

Training Trace - Epoch by Epoch

Epoch 1: ******
Epoch 2: **********
Epoch 3: **************
Epoch 4: ****************
Epoch 5: ******************
(Loss decreasing, accuracy increasing)

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.55	Model starts learning to match queries and documents
2	0.48	0.68	Loss decreases and accuracy improves as embeddings get better
3	0.35	0.78	Model shows good convergence with improved retrieval quality
4	0.28	0.83	Further refinement of embeddings and similarity scoring
5	0.22	0.87	Training stabilizes with high accuracy and low loss

Prediction Trace - 5 Layers

Layer 1: Input Queries

Layer 2: Query Embedding

Layer 3: Similarity Calculation

Layer 4: Score Aggregation

Layer 5: Ranking and Retrieval

Model Quiz - 3 Questions

Why do we convert queries and documents into vectors?

A To make them readable by humans

B To compare them mathematically

C To reduce their size

D To encrypt the data

Key Insight

Multi-query retrieval improves search by combining multiple questions to better match documents. Embedding queries and documents into vectors allows easy comparison. Training reduces loss and increases accuracy, making retrieval more precise.

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