Why RAG gives agents knowledge in Agentic AI - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load documents for retrieval in RAG.

Agentic AI

from langchain.document_loaders import TextLoader
loader = TextLoader('[1]')
docs = loader.load()

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

Aagent.py

Bdata.txt

Cconfig.json

Dmodel.py

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a code or config file instead of a text file.

The documents to retrieve knowledge from must be loaded from a text file, so 'data.txt' is correct.

2fill in blank

medium

Complete the code to create a retriever from documents for RAG.

Agentic AI

from langchain.vectorstores import FAISS
retriever = FAISS.from_documents(docs, [1]).as_retriever()

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

Aloss_function

Btokenizer

Coptimizer

Dembedding_model

Attempts:

3 left

💡 Hint

Common Mistakes

Using tokenizer or optimizer which are unrelated to vector search.

The retriever needs an embedding model to convert documents into vectors for similarity search.

3fill in blank

hard

Fix the error in the RAG agent creation by completing the missing argument.

Agentic AI

from langchain.chains import RetrievalQA
rag_agent = RetrievalQA.from_chain_type(llm=llm, retriever=[1], return_source_documents=True)

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

Aretriever

Bembedding_model

Ctokenizer

Doptimizer

Attempts:

3 left

💡 Hint

Common Mistakes

Passing tokenizer or embedding model instead of retriever.

The RAG agent requires a retriever to fetch relevant documents for answering queries.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that filters documents by length and stores their text.

Agentic AI

filtered_docs = {doc.metadata['title']: doc.page_content for doc in docs if len(doc.page_content) [1] 100 and doc.metadata['source'] [2] 'trusted_source'}

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

A<

B>

C==

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '>' for length or '!=' for source causes wrong filtering.

We filter documents shorter than 100 characters and from the trusted source.

5fill in blank

hard

Fill all three blanks to build a RAG pipeline that embeds, retrieves, and answers questions.

Agentic AI

embedding = [1]()
retriever = FAISS.from_documents(docs, embedding).as_retriever()
rag_agent = RetrievalQA.from_chain_type(llm=[2](), retriever=retriever, return_source_documents=True)
answer = rag_agent.run([3])

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

AOpenAIEmbeddings

BOpenAI

C'What is RAG?'

DGPT2

Attempts:

3 left

💡 Hint

Common Mistakes

Using GPT2 as LLM or passing a non-string question.

The embedding model is OpenAIEmbeddings, the language model is OpenAI, and the question is a string.

Practice

1. What is the main reason RAG (Retrieval-Augmented Generation) helps AI agents have better knowledge?

easy

A. It ignores external information sources.

B. It only uses pre-trained data without updates.

C. It combines retrieving information with generating answers.

D. It relies solely on random guessing.

Solution

1. Step 1: Understand RAG's components

   RAG combines two parts: retrieval (finding relevant info) and generation (creating answers).

2. Step 2: Connect combination to knowledge improvement

   By mixing retrieval and generation, agents can use both stored and new info, improving knowledge.

3. Final Answer:

   It combines retrieving information with generating answers. -> Option C

4. Quick Check:

   RAG = retrieval + generation [OK]

Hint: Remember RAG mixes retrieval and generation [OK]

Common Mistakes:

• Thinking RAG only uses pre-trained data
• Believing RAG ignores external info
• Assuming RAG guesses randomly

2. Which of the following is the correct way to describe RAG's process in simple terms?

easy

A. RAG retrieves relevant documents, then generates answers using them.

B. RAG generates answers first, then searches for info.

C. RAG only retrieves documents without generating answers.

D. RAG randomly selects answers without retrieval.

Solution

1. Step 1: Identify RAG's sequence

   RAG first retrieves relevant documents from a source.

2. Step 2: Understand generation step

   Then it generates answers based on the retrieved documents.

3. Final Answer:

   RAG retrieves relevant documents, then generates answers using them. -> Option A

4. Quick Check:

   Retrieve then generate [OK]

Hint: RAG retrieves first, then generates answers [OK]

Common Mistakes:

• Thinking generation happens before retrieval
• Believing RAG only retrieves without generation
• Assuming random answer selection

3. Given this simplified code snippet for a RAG agent:

retrieved_docs = ['Doc about cats', 'Doc about dogs']
query = 'Tell me about cats'
answer = generate_answer(query, retrieved_docs)
print(answer)

What is the expected output behavior?

medium

A. The answer will only use the query without documents.

B. The answer will ignore retrieved_docs and be random.

C. The code will cause an error because generate_answer is undefined.

D. The answer will be generated using information about cats and dogs.

Solution

1. Step 1: Understand inputs to generate_answer

   The function gets the query and the retrieved documents about cats and dogs.

2. Step 2: Predict output behavior

   Since retrieved_docs include relevant info, the answer will use that info to respond about cats.

3. Final Answer:

   The answer will be generated using information about cats and dogs. -> Option D

4. Quick Check:

   RAG uses retrieved docs to generate answers [OK]

Hint: Check if retrieved docs are used in generation [OK]

Common Mistakes:

• Assuming generate_answer is undefined error
• Thinking answer ignores retrieved docs
• Believing answer is random

4. Consider this code snippet for a RAG agent:

def rag_agent(query):
    docs = retrieve_docs(query)
    answer = generate_answer(docs)
    return answer

print(rag_agent('What is AI?'))

What is the main error in this code?

medium

A. generate_answer is called without the query parameter.

B. retrieve_docs is missing the query argument.

C. rag_agent returns docs instead of answer.

D. print statement is outside the function.

Solution

1. Step 1: Check function calls and parameters

   retrieve_docs is called with query, which is correct.

2. Step 2: Identify generate_answer call issue

   generate_answer is called with only docs, but it needs both query and docs to generate a proper answer.

3. Final Answer:

   generate_answer is called without the query parameter. -> Option A

4. Quick Check:

   generate_answer needs query and docs [OK]

Hint: Check if all required parameters are passed to functions [OK]

Common Mistakes:

• Thinking retrieve_docs lacks argument
• Believing rag_agent returns wrong value
• Confusing print statement placement

5. How does RAG improve an AI agent's ability to answer questions about recent events not in its training data?

hard

A. By only relying on its fixed training data without updates.

B. By retrieving up-to-date documents and generating answers using them.

C. By guessing answers based on old data patterns.

D. By ignoring external information and focusing on generation.

Solution

1. Step 1: Understand RAG's retrieval role

   RAG retrieves current documents from external sources, including recent events.

2. Step 2: Understand generation with new info

   It then generates answers using this fresh info, allowing it to handle new questions accurately.

3. Final Answer:

   By retrieving up-to-date documents and generating answers using them. -> Option B

4. Quick Check:

   RAG uses fresh retrieval for new knowledge [OK]

Hint: Remember RAG updates knowledge via retrieval [OK]

Common Mistakes:

• Thinking RAG only uses old training data
• Assuming RAG guesses without info
• Believing RAG ignores external data