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Python / Python Modern Generative AI and Agents Interview Questions

What are LangChain's core abstractions — Chains, Runnables, and the LangChain Expression Language?

LangChain's modern design (LangChain v0.2+) revolves around the Runnable interface: any component that can be invoked (prompts, LLMs, parsers, retrievers, custom functions) implements invoke(), stream(), and batch(). The LangChain Expression Language (LCEL) composes Runnables with the pipe operator |, producing a new Runnable that executes components left-to-right, automatically supporting streaming, async, and batch invocation.

This replaces the legacy LLMChain class with a more composable and transparent design. Every step is inspectable, every component is swappable, and the chain is serialisable for deployment with LangServe.

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser, JsonOutputParser
from langchain_core.runnables import RunnableLambda, RunnableParallel

llm = ChatOpenAI(model='gpt-4o-mini')

# ── Simple chain: prompt | llm | parser
prompt = ChatPromptTemplate.from_messages([
    ('system', 'You are a concise technical writer.'),
    ('user',   'Write a one-sentence definition of {concept}.'),
])
chain = prompt | llm | StrOutputParser()
print(chain.invoke({'concept': 'transformer attention'}))

# ── Streaming output
for chunk in chain.stream({'concept': 'gradient descent'}):
    print(chunk, end='', flush=True)

# ── Batch invocation (runs concurrently)
results = chain.batch([
    {'concept': 'RAG'},
    {'concept': 'fine-tuning'},
    {'concept': 'embeddings'},
])

# ── Parallel execution: run two chains simultaneously
summary_chain = (
    ChatPromptTemplate.from_template('Summarise: {text}') | llm | StrOutputParser()
)
keywords_chain = (
    ChatPromptTemplate.from_template('List 5 keywords from: {text}') | llm | StrOutputParser()
)
parallel = RunnableParallel(
    summary=summary_chain,
    keywords=keywords_chain,
)
result = parallel.invoke({'text': 'Attention mechanisms allow models to focus...'})
print(result['summary'])
print(result['keywords'])

What does chain.batch() do differently from calling chain.invoke() in a loop?
What is the key advantage of LCEL's Runnable interface over the legacy LLMChain class?

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