Python / Python Modern Generative AI and Agents Interview Questions

What is LlamaIndex and how does it compare to LangChain for RAG use cases?

LlamaIndex (formerly GPT Index) is a data framework specialised for connecting LLMs to diverse data sources. While LangChain is a general-purpose composable LLM framework covering agents, chains, memory, and RAG, LlamaIndex focuses almost exclusively on the data ingestion and indexing layer — providing more sophisticated out-of-the-box RAG patterns like query routing, recursive retrieval, and knowledge graphs.

# pip install llama-index llama-index-llms-openai llama-index-embeddings-openai
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding

# ââ Configure global settings
Settings.llm       = OpenAI(model='gpt-4o-mini', temperature=0)
Settings.embed_model = OpenAIEmbedding(model='text-embedding-3-small')
Settings.chunk_size = 1024

# ââ Load and index documents in 3 lines
docs    = SimpleDirectoryReader('./docs').load_data()
index   = VectorStoreIndex.from_documents(docs)    # embeds and indexes
engine  = index.as_query_engine()                  # wraps retriever + LLM

response = engine.query('What are the key conclusions of the report?')
print(response.response)
print(response.source_nodes[0].text[:200])  # retrieved passage

# ââ Persist index to disk and reload
index.storage_context.persist('./index_store')

from llama_index.core import StorageContext, load_index_from_storage
storage = StorageContext.from_defaults(persist_dir='./index_store')
index2  = load_index_from_storage(storage)

# ââ Advanced: Sub-question engine (breaks complex queries into sub-queries)
from llama_index.core.query_engine import SubQuestionQueryEngine
from llama_index.core.tools import QueryEngineTool

q_tool = QueryEngineTool.from_defaults(query_engine=engine,
                                        description='Annual report 2024')
sub_engine = SubQuestionQueryEngine.from_defaults(query_engine_tools=[q_tool])
resp = sub_engine.query('Compare revenue and profit growth, then summarise trends.')
print(resp.response)

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What is the main focus of LlamaIndex compared to LangChain?LlamaIndex is a competitor that implements the same features faster

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LlamaIndex specialises in the data layer — document loading, indexing, structured retrieval, and query routing — with more sophisticated out-of-the-box RAG patterns; LangChain is broader, covering agents, chains, memory, and RAG with more flexibility but less RAG-specific depth

✓ Correct! Well done.

LlamaIndex only supports OpenAI models while LangChain supports all providers

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LlamaIndex requires a paid subscription while LangChain is free

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What does the SubQuestionQueryEngine in LlamaIndex do?It decomposes the query into multiple sub-queries, runs each against the index, and synthesises a combined answer — useful for complex questions requiring information from multiple sections or comparisons across documents

✓ Correct! Well done.

It limits each query to a single matching document

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It generates follow-up questions to ask the user for clarification

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It runs the same query against multiple different LLMs and returns the majority answer

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Python / Python Modern Generative AI and Agents Interview Questions

What is LlamaIndex and how does it compare to LangChain for RAG use cases?

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