Python / Python Modern Generative AI and Agents Interview Questions

How do you manage, version, and reuse prompts in production LLM applications?

In production systems, prompts are first-class assets — they evolve through experimentation, need version control, and may be shared across teams. Hard-coding prompts in application code makes them difficult to update without deployment. Several strategies improve prompt management.

# ── Approach 1: LangChain Hub (versioned, shareable prompt registry)
from langchain import hub

# Pull a community prompt by handle (owner/prompt-name:commit-hash)
rag_prompt = hub.pull('rlm/rag-prompt')
print(rag_prompt.messages[0].prompt.template)

# ── Approach 2: PromptTemplate with variables
from langchain_core.prompts import PromptTemplate, ChatPromptTemplate
from langchain_core.prompts import FewShotChatMessagePromptTemplate

# Parameterised template
qa_template = PromptTemplate.from_template(
    'You are an expert in {domain}. Answer the following question concisely.\n\n'
    'Question: {question}\n'
    'Answer:'
)
formatted = qa_template.format(domain='astrophysics', question='What is a black hole?')

# ── Few-shot template
examples = [
    {'input': 'happy',   'output': 'sad'},
    {'input': 'tall',    'output': 'short'},
    {'input': 'energetic','output': 'lethargic'},
]
example_prompt = ChatPromptTemplate.from_messages([
    ('human', '{input}'),
    ('ai',    '{output}'),
])
few_shot_prompt = FewShotChatMessagePromptTemplate(
    example_prompt=example_prompt,
    examples=examples,
)
final_prompt = ChatPromptTemplate.from_messages([
    ('system', 'Give the antonym of each word.'),
    few_shot_prompt,
    ('human', '{word}'),
])
print(final_prompt.invoke({'word': 'joyful'}).to_messages())

# ── Approach 3: LangSmith for prompt tracing and experimentation
# Set env vars: LANGCHAIN_API_KEY, LANGCHAIN_TRACING_V2=true
# Every chain invocation is automatically logged to LangSmith dashboard
# enabling side-by-side comparison of prompt versions

Prompts evolve independently of application logic through experimentation — treating them as versioned, separate assets enables iteration, A/B testing, and rollback without code deployment, and allows non-developers to contribute prompt improvements

✓ Correct! Well done.

The LangChain Hub requires prompts to be stored externally to function

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Prompt strings are too large to include in Python source files

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What advantage does FewShotChatMessagePromptTemplate offer over manually concatenating examples in a string?FewShotChatMessagePromptTemplate automatically generates examples from the training data

✗ Try again.

It structures examples as properly-typed Human/AI message pairs that the model interprets correctly as conversation turns, rather than raw text that may confuse the model about formatting; it also makes the example list programmatically manageable and reusable

✓ Correct! Well done.

It limits the number of examples to the model's context window automatically

✗ Try again.

It randomly selects examples from a larger pool for each call

✗ Try again.

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Why should production prompts be managed separately from application code?Prompts cause syntax errors if included directly in Python source files

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More Related questions...

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