Why RAG grounds LLMs in real data in Prompt Engineering / GenAI - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the RAG model class from transformers.

Prompt Engineering / GenAI

from transformers import [1]

Drag options to blanks, or click blank then click option'

ARagTokenizer

BRagSequenceForGeneration

CRagRetriever

DRagModel

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing tokenizer or retriever classes instead of the main model.

Confusing RagSequenceForGeneration with RagModel.

The RagModel class is the core RAG model that combines retrieval and generation.

2fill in blank

medium

Complete the code to initialize the retriever for RAG using a dataset.

Prompt Engineering / GenAI

retriever = RagRetriever.from_pretrained('facebook/rag-token-base', index_name=[1])

Drag options to blanks, or click blank then click option'

A'exact'

B'custom'

C'legacy'

D'default'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'custom' or 'default' which are not valid index names.

Confusing 'legacy' with the correct retrieval method.

The exact index_name tells the retriever to use exact matching for retrieval.

3fill in blank

hard

Fix the error in the code to generate an answer grounded on retrieved documents.

Prompt Engineering / GenAI

outputs = model.generate(input_ids, context_doc_ids=[1])

Drag options to blanks, or click blank then click option'

Aretrieved_docs

Bretrieved_doc_embeds

Cretrieved_doc_ids

Dretrieved_doc_texts

Attempts:

3 left

💡 Hint

Common Mistakes

Passing raw document texts instead of IDs.

Passing embeddings which are not accepted here.

The context_doc_ids argument expects the IDs of retrieved documents, not the raw documents or embeddings.

4fill in blank

hard

Fill both blanks to create a RAG model with retriever and generate output.

Prompt Engineering / GenAI

model = RagSequenceForGeneration.from_pretrained('facebook/rag-token-base', retriever=[1])
outputs = model.generate([2])

Drag options to blanks, or click blank then click option'

Aretriever

Binput_ids

Cinput_texts

Dtokenizer

Attempts:

3 left

💡 Hint

Common Mistakes

Passing raw texts instead of token IDs to generate.

Not passing the retriever when creating the model.

The model needs the retriever object to fetch documents, and input_ids to generate answers.

5fill in blank

hard

Fill all three blanks to tokenize input, retrieve docs, and generate grounded output.

Prompt Engineering / GenAI

inputs = tokenizer([1], return_tensors='pt')
retrieved_docs = retriever.retrieve([2])
outputs = model.generate(input_ids=inputs['input_ids'], context_doc_ids=[3])

Drag options to blanks, or click blank then click option'

A'What is RAG?'

Binputs['input_ids']

Cretrieved_docs['doc_ids']

Dretrieved_docs['texts']

Attempts:

3 left

💡 Hint

Common Mistakes

Using raw texts instead of token IDs for retrieval or generation.

Mixing up document texts and document IDs.

First, we tokenize the question string. Then we retrieve documents using the token IDs. Finally, we generate output using input IDs and retrieved document IDs.

Practice

1. What is the main purpose of Retrieval-Augmented Generation (RAG) in large language models?

easy

A. To make the model run faster by skipping data retrieval

B. To connect the model to real data for more accurate answers

C. To reduce the size of the language model

D. To generate random text without any input

Solution

1. Step 1: Understand RAG's role

   RAG helps language models by retrieving relevant real data before generating answers.

2. Step 2: Connect purpose to options

   Only To connect the model to real data for more accurate answers mentions connecting to real data for accuracy, which matches RAG's goal.

3. Final Answer:

   To connect the model to real data for more accurate answers -> Option B

4. Quick Check:

   RAG purpose = connect to real data [OK]

Hint: RAG links models to real info for better answers [OK]

Common Mistakes:

• Thinking RAG speeds up model without retrieval
• Confusing RAG with model size reduction
• Believing RAG generates random text

2. Which step is NOT part of the RAG process in grounding LLMs?

easy

A. Retrieving relevant documents from a database

B. Adding retrieved information to the model's input

C. Generating output based on combined input and data

D. Training the model from scratch every time

Solution

1. Step 1: Recall RAG process steps

   RAG retrieves data, adds it to input, then generates output without retraining.

2. Step 2: Identify the incorrect step

   Training the model from scratch every time says training from scratch every time, which is not part of RAG's normal use.

3. Final Answer:

   Training the model from scratch every time -> Option D

4. Quick Check:

   RAG skips retraining each query [OK]

Hint: RAG retrieves and generates, no retraining each time [OK]

Common Mistakes:

• Confusing retrieval with training
• Thinking RAG modifies model weights every query
• Ignoring the retrieval step

3. Given this simplified RAG workflow code snippet, what will be printed?

retrieved_docs = ['Data about cats', 'Info on dogs']
input_text = 'Tell me about pets.'
combined_input = input_text + ' ' + ' '.join(retrieved_docs)
print(combined_input)

medium

A. Tell me about pets. Data about cats Info on dogs

B. Tell me about pets.['Data about cats', 'Info on dogs']

C. Tell me about pets.Data about catsInfo on dogs

D. Error: cannot join list of strings

Solution

1. Step 1: Understand string join operation

   ' '.join(retrieved_docs) joins list items with spaces, producing 'Data about cats Info on dogs'.

2. Step 2: Combine input_text and joined string

   Adding input_text + ' ' + joined string results in 'Tell me about pets. Data about cats Info on dogs'.

3. Final Answer:

   Tell me about pets. Data about cats Info on dogs -> Option A

4. Quick Check:

   Join list with spaces = combined string [OK]

Hint: Join list with spaces to combine text [OK]

Common Mistakes:

• Printing list directly without join
• Missing spaces between strings
• Assuming join causes error

4. Identify the error in this RAG-like code snippet:

def rag_generate(input_text, docs):
    combined = input_text + docs
    return combined

print(rag_generate('Info:', ['doc1', 'doc2']))

medium

A. Function missing return statement

B. docs should be a string, not a list

C. Cannot add string and list directly

D. No error, code runs fine

Solution

1. Step 1: Check data types in addition

   input_text is a string, docs is a list; Python cannot add string + list directly.

2. Step 2: Identify error cause

   Adding string and list causes a TypeError, so Cannot add string and list directly is correct.

3. Final Answer:

   Cannot add string and list directly -> Option C

4. Quick Check:

   String + list = TypeError [OK]

Hint: Check data types before adding strings and lists [OK]

Common Mistakes:

• Thinking list concatenation works with strings
• Ignoring Python type errors
• Assuming function lacks return

5. In a RAG system, why is it important to ground the language model with up-to-date external data rather than relying solely on its training data?

hard

A. Because training data may be outdated and miss recent facts

B. Because external data makes the model run faster

C. Because training data is always incorrect

D. Because grounding removes the need for any model training

Solution

1. Step 1: Understand training data limits

   Models learn from fixed training data that can become outdated over time.

2. Step 2: Explain grounding benefit

   Grounding with fresh external data helps provide current, accurate answers beyond training knowledge.

3. Final Answer:

   Because training data may be outdated and miss recent facts -> Option A

4. Quick Check:

   Grounding updates info beyond training data [OK]

Hint: Grounding updates model with fresh facts [OK]

Common Mistakes:

• Thinking external data speeds up model
• Believing training data is always wrong
• Assuming grounding replaces training