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Python / Python Modern Generative AI and Agents Interview Questions

What is prompt engineering and what are the most effective techniques for getting better outputs from LLMs?

Prompt engineering is the practice of crafting inputs to LLMs to elicit more accurate, relevant, and reliable outputs without changing the model's weights. Since LLMs are sensitive to the exact phrasing, structure, and context of the prompt, small changes can dramatically affect output quality.

Core Prompt Engineering Techniques
TechniqueDescriptionWhen to use
Zero-shotDirect question with no examplesSimple tasks the model handles well
Few-shot2–5 input-output examples in the prompt before the querySpecific output format; tasks needing consistency
Chain-of-Thought (CoT)Prompt with 'Let's think step by step' or examples showing reasoningMath, logic, multi-step reasoning
Role promptingSystem prompt: 'You are an expert Python developer'Tonality and expertise alignment
Output format constraintInstruct model to respond in JSON / a specific schemaDownstream parsing
Self-consistencySample k responses, majority-vote the answerReducing hallucination on factual Q&A
from openai import OpenAI

client = OpenAI()

# ── Few-shot prompting
few_shot_prompt = '''Classify the sentiment of each review as POSITIVE or NEGATIVE.

Review: 'This headset has amazing sound quality and fits perfectly.'
Sentiment: POSITIVE

Review: 'Stopped working after two days. Very disappointed.'
Sentiment: NEGATIVE

Review: '{user_review}'
Sentiment:'''

# ── Chain-of-Thought prompting
cot_prompt = (
    'A train travels 120 miles in 2 hours, then 90 miles in 1.5 hours. '
    'What is its average speed for the entire journey? '
    'Think through this step by step before giving the final answer.'
)

# ── Structured / JSON output
structured_prompt = (
    'Extract the company name, role, and years of experience from this text. '
    'Return ONLY valid JSON matching this schema: '
    '{"company": str, "role": str, "years": int}\n\n'
    'Text: She worked at Acme Corp as a senior engineer for 5 years.'
)

resp = client.chat.completions.create(
    model='gpt-4o',
    messages=[{'role': 'user', 'content': structured_prompt}],
    temperature=0,           # deterministic for parsing tasks
    response_format={'type': 'json_object'},  # enforces JSON output
)
import json
data = json.loads(resp.choices[0].message.content)
print(data)  # {'company': 'Acme Corp', 'role': 'senior engineer', 'years': 5}

Why is temperature=0 recommended for tasks that require structured output like JSON?
What is the Chain-of-Thought (CoT) prompting technique and why does it improve reasoning?

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