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2.1 Anatomy of a Prompt

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2.1 Anatomy of a Prompt

Key Concepts: System prompt · User prompt · Roles · Instruction clarity

Official Docs: OpenAI Prompt Engineering Guide · Anthropic Prompt Engineering

The Chat Completion Format​

All modern LLM APIs use a messages array with three role types:

messages = [
    {
        "role": "system",
        "content": "You are a senior Python engineer. Be concise and use type hints."
    },
    {
        "role": "user",
        "content": "Write a function that validates an email address."
    },
]
RolePurpose
systemSets the model's persona, rules, and global context
userThe human's input or task
assistantPrior model responses (real or injected as examples)

The System Prompt​

The system prompt is your contract with the model. It should specify:

  1. Persona — who the model is
  2. Task scope — what it should and should not do
  3. Output format — JSON, markdown, plain text
  4. Tone and constraints — formal/casual, word limit

Weak vs Strong System Prompt​

# ❌ Weak
"You are a helpful assistant."

# ✅ Strong
"You are a code review assistant for a Python 3.12 codebase.
Always:
- Point out security issues first
- Suggest the simplest fix
- Keep responses under 200 words
- Use inline code blocks for all code
Never suggest third-party libraries unless asked."

Instruction Clarity Principles​

From the OpenAI prompt engineering guide:

1. Use delimiters to separate data from instructions

prompt = """
Summarise the following article in exactly 3 bullet points.

ARTICLE:
"""
{article_text}
"""
"""

2. Specify the output format explicitly

Respond ONLY with valid JSON matching this schema:
{"sentiment": "positive|negative|neutral", "confidence": 0.0-1.0}

3. Give the model an escape hatch

If you cannot answer from the provided context, respond with:
{"answer": null, "reason": "insufficient context"}

Multi-Turn Conversations​

from openai import OpenAI

client = OpenAI()
history = [{"role": "system", "content": "You are a Python tutor."}]

def chat(user_input: str) -> str:
    history.append({"role": "user", "content": user_input})
    resp = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=history,
    )
    answer = resp.choices[0].message.content
    history.append({"role": "assistant", "content": answer})
    return answer

print(chat("What is a list comprehension?"))
print(chat("Show me an example with filtering."))
Context Window Management

History grows with every turn. Implement a sliding window or summarisation strategy before the context fills up.

Key Takeaways​

  • System prompt = persona + rules + format constraints
  • Use delimiters to cleanly separate data from instructions
  • Always specify output format explicitly
  • Manage history length to avoid context overflow

Further Reading​

Next → 2.2 Core Prompting Techniques