Basic RAG chain with LCEL in LangChain - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the main purpose of a Retrieval-Augmented Generation (RAG) chain?

In simple terms, what does a RAG chain do in a language model system?

A It compresses large documents into smaller summaries without generating new text.

B It only generates text without using any external information.

C It translates text from one language to another without retrieving documents.

D It retrieves relevant documents and uses them to generate more accurate answers.

💡 Hint

Think about how RAG combines searching and writing.

A RAG chain first finds useful information from documents, then uses that info to create better answers.

❓ Predict Output

intermediate

Output of a simple RAG chain

Given the following code snippet using LangChain, what will be the output?

LangChain

from langchain.chains import RetrievalQA
from langchain.llms import OpenAI

retriever = DummyRetriever()
qa = RetrievalQA.from_chain_type(llm=OpenAI(temperature=0), chain_type="stuff", retriever=retriever)

query = "What is AI?"
result = qa.run(query)
print(result)

A SyntaxError: invalid syntax in chain_type argument

B "AI is a type of hardware used in computers."

C "AI stands for Artificial Intelligence, which is the simulation of human intelligence by machines."

D TypeError: 'DummyRetriever' object is not callable

💡 Hint

Assume DummyRetriever returns a document defining AI correctly.

The retriever provides a document defining AI, and the LLM generates the answer based on it.

❓ Hyperparameter

advanced

Choosing the best temperature setting for a RAG chain

In a RAG chain using LCEL, which temperature setting is best for producing consistent factual answers?

A Temperature = 0 (lowest randomness)

B Temperature = 1 (highest randomness)

C Temperature = 0.7 (medium randomness)

D Temperature = -1 (invalid value)

💡 Hint

Lower temperature means less creativity, more factual.

Setting temperature to 0 makes the model deterministic and factual, ideal for RAG chains.

❓ Metrics

advanced

Evaluating RAG chain performance with accuracy

You run a RAG chain on 100 questions. It answers 85 correctly. What is the accuracy?

A 15%

B 85%

C 100%

D Cannot be determined without loss value

💡 Hint

Accuracy = (correct answers / total questions) * 100

85 correct out of 100 means accuracy is 85%.

🔧 Debug

expert

Debugging a RAG chain that returns empty answers

Given this snippet, why does the RAG chain return empty answers?

retriever = SomeRetriever()
qa_chain = RetrievalQA.from_chain_type(llm=OpenAI(), retriever=retriever)
answer = qa_chain.run("")
print(answer)

A The query string is empty, so no documents are retrieved or answers generated.

B The retriever is not initialized properly, causing a runtime error.

C The OpenAI model is not connected to the internet.

D The chain_type argument is missing, causing a syntax error.

💡 Hint

Check the input query string.

An empty query means no search happens, so no answer is generated.

Practice

1. What is the main purpose of a RAG chain in Langchain?

easy

A. To combine document search with AI to answer questions

B. To train a new AI model from scratch

C. To visualize data in charts

D. To clean and preprocess raw data

Solution

1. Step 1: Understand RAG chain function

   A RAG (Retrieval-Augmented Generation) chain uses search to find relevant documents and AI to generate answers.

2. Step 2: Match purpose with options

   Only To combine document search with AI to answer questions describes combining search and AI to answer questions, which is the core of RAG chains.

3. Final Answer:

   To combine document search with AI to answer questions -> Option A

4. Quick Check:

   RAG chain = combine search + AI [OK]

Hint: RAG means search plus AI for answers [OK]

Common Mistakes:

• Thinking RAG trains new models
• Confusing RAG with data visualization
• Assuming RAG only cleans data

2. Which of the following is the correct way to create a RAG chain using LCEL in Langchain?

easy

A. rag_chain = LCEL.RAGChain.build(docs, llm)

B. rag_chain = LCEL.RAGChain.from_documents(docs, llm)

C. rag_chain = RAGChain(docs, llm).build()

D. rag_chain = LCEL.createRAG(docs, llm)

Solution

1. Step 1: Recall LCEL syntax for RAG chain

   LCEL provides a method called from_documents to create a RAG chain from documents and an LLM.

2. Step 2: Compare options with correct syntax

   Only rag_chain = LCEL.RAGChain.from_documents(docs, llm) uses LCEL.RAGChain.from_documents(docs, llm), which is the correct method.

3. Final Answer:

   rag_chain = LCEL.RAGChain.from_documents(docs, llm) -> Option B

4. Quick Check:

   LCEL.RAGChain.from_documents = correct syntax [OK]

Hint: Use from_documents method to create RAG chain [OK]

Common Mistakes:

• Using non-existent createRAG method
• Calling build() on RAGChain instance
• Mixing method names or class names

3. Given the code snippet:

docs = ["Doc1 content", "Doc2 content"]
llm = SomeLLM()
rag_chain = LCEL.RAGChain.from_documents(docs, llm)
answer = rag_chain.run("What is in Doc1?")
print(answer)

What is the expected output?

medium

A. An error because docs should be a string, not a list

B. A list of documents including Doc1 and Doc2

C. An answer generated by the LLM based on Doc1 content

D. The string 'What is in Doc1?' printed as is

Solution

1. Step 1: Understand RAG chain run method

   Running rag_chain.run(question) uses the LLM to answer based on retrieved documents.

2. Step 2: Analyze input and output

   Docs are provided as a list, which is valid. The output is an AI-generated answer about Doc1 content.

3. Final Answer:

   An answer generated by the LLM based on Doc1 content -> Option C

4. Quick Check:

   rag_chain.run(question) = AI answer [OK]

Hint: RAG chain answers questions using docs + LLM [OK]

Common Mistakes:

• Expecting raw docs list as output
• Thinking docs must be a single string
• Assuming run just echoes input question

4. You wrote this code:

rag_chain = LCEL.RAGChain.from_documents("doc.txt", llm)
answer = rag_chain.run("Explain the content.")

But it raises an error. What is the likely problem?

medium

A. The llm variable is not defined

B. LCEL.RAGChain does not have from_documents method

C. The run method requires two arguments

D. The documents argument should be a list, not a filename string

Solution

1. Step 1: Check documents argument type

   from_documents expects a list of document strings, not a filename string.

2. Step 2: Identify error cause

   Passing "doc.txt" as a string causes an error because it is not a list of documents.

3. Final Answer:

   The documents argument should be a list, not a filename string -> Option D

4. Quick Check:

   Documents must be list, not filename string [OK]

Hint: Pass list of docs, not filename string [OK]

Common Mistakes:

• Passing filename instead of document list
• Assuming run needs two arguments
• Thinking from_documents method is missing

5. You want to build a RAG chain that answers questions from multiple document types (PDF, text, and web pages) using LCEL. Which approach correctly prepares the documents before creating the RAG chain?

hard

A. Load and convert each document type to text, combine into a list, then pass to LCEL.RAGChain.from_documents

B. Pass file paths directly to LCEL.RAGChain.from_documents without loading

C. Use only one document type to avoid errors

D. Create separate RAG chains for each document type and run them independently

Solution

1. Step 1: Understand document preparation for RAG

   RAG chains require documents as text strings, so all document types must be converted to text first.

2. Step 2: Combine and pass documents correctly

   After conversion, combine all texts into a list and pass to LCEL.RAGChain.from_documents for a single chain.

3. Final Answer:

   Load and convert each document type to text, combine into a list, then pass to LCEL.RAGChain.from_documents -> Option A

4. Quick Check:

   Convert all docs to text list before RAG chain [OK]

Hint: Convert all docs to text list before RAG chain [OK]

Common Mistakes:

• Passing file paths instead of text
• Using only one document type unnecessarily
• Creating multiple chains instead of one combined