Editing Prompt Engineering (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Prompt templates for common use cases:'''

; Zero-shot classification
<syntaxhighlight lang="python">
prompt = """Classify the sentiment of the following customer review.
Return ONLY one of: POSITIVE, NEGATIVE, NEUTRAL.

Review: "{review_text}"

Sentiment:"""
</syntaxhighlight>

; Chain-of-thought for math/reasoning
<syntaxhighlight lang="python">
prompt = """Solve this problem step by step. Show your reasoning clearly.
After working through it, state your final answer on a new line beginning with "Answer:".

Problem: A train travels at 60 mph for 2 hours, then at 90 mph for 1.5 hours.
What is the total distance traveled?"""
# Model will reason: "60×2=120, 90×1.5=135, total=255 miles" → Answer: 255 miles
</syntaxhighlight>

; Structured output (JSON)
<syntaxhighlight lang="python">
prompt = """Extract the following information from the job posting below.
Return ONLY valid JSON with exactly these fields:
{
  "job_title": string,
  "company": string,
  "location": string,
  "salary_range": string or null,
  "required_skills": [list of strings],
  "experience_years": number or null
}

Job Posting:
---
{job_posting_text}
---

JSON:"""
</syntaxhighlight>

; Few-shot prompting
<syntaxhighlight lang="python">
prompt = """Convert informal English to formal business English.

Informal: "Hey, just wanted to check in on that report thing"
Formal: "I am writing to inquire about the status of the report."

Informal: "Can you send that over ASAP?"
Formal: "Could you please expedite the delivery of the requested document?"

Informal: "{user_input}"
Formal:"""
</syntaxhighlight>

; Key prompt engineering techniques reference
: '''Chain-of-thought''' → "Think step by step" / "Let's reason through this"
: '''Self-consistency''' → Sample 5–10 times at temp=0.7, take majority answer
: '''Tree of Thoughts''' → Explore multiple reasoning branches, evaluate, backtrack
: '''Least-to-most''' → Break complex task into sub-tasks, solve sequentially
: '''Generated Knowledge''' → Ask model to generate relevant facts first, then answer
: '''Directional Stimulus''' → Include a hint or keyword that steers output direction
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