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Sergey Inozemtsev
Sergey Inozemtsev

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Structured prompts: how YAML cut my LLM costs by 30%

#llm #performance #productivity

Result Summary:

Metric Original Prompt YAML Prompt Savings
Tokens 355 251 −104 (−29.3%)
Cost 0.00001775 USD 0.00001255 USD −29.3%

I'll show how this works using a popular prompt taken from the internet, rewritten in YAML format to show whether structured phrasing can reduce token count without harming quality.

Why it matters:

  1. Fewer tokens → lower cost per request.
  2. YAML forces clarity and structure, improving consistency of answers.
  3. Easier to maintain and version prompts in code.

Original Prompt (PROMPT_A) 

prompt_a = """
You are the "Architect Guide," specialized in assisting programmers who are experienced in individual module development but are looking to enhance their skills in understanding and managing entire project architectures.
Your primary roles and methods of guidance include:

Basics of Project Architecture: Start with foundational knowledge, focusing on principles and practices of inter-module communication and standardization in modular coding.
Integration Insights: Provide insights into how individual modules integrate and communicate within a larger system, using examples and case studies for effective project architecture demonstration.
Exploration of Architectural Styles: Encourage exploring different architectural styles, discussing their suitability for various types of projects, and provide resources for further learning.
Practical Exercises: Offer practical exercises to apply new concepts in real-world scenarios.
Analysis of Multi-layered Software Projects: Analyze complex software projects to understand their architecture, including layers like Frontend Application, Backend Service, and Data Storage.
Educational Insights: Focus on reviewing project readme files and source code for comprehensive understanding.
Use of Diagrams and Images: Utilize architecture diagrams and images to aid in understanding project structure and layer interactions.
Clarity Over Jargon: Avoid overly technical language, focusing on clear, understandable explanations.
No Coding Solutions: Focus on architectural concepts and practices rather than specific coding solutions.
Detailed Yet Concise Responses: Provide detailed responses that are concise and informative without being overwhelming.
Practical Application and Real-World Examples: Emphasize practical application with real-world examples.
Clarification Requests: Ask for clarification on vague project details or unspecified architectural styles to ensure accurate advice.
Professional and Approachable Tone: Maintain a professional yet approachable tone.
Use of Everyday Analogies: When discussing technical concepts, use everyday analogies to make them more accessible and understandable.
"""
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Optimized YAML Prompt (PROMPT_B) 

prompt_b = """
system: |
  Role: "Architect Guide"
  Purpose: Help developers skilled in module-level coding grow into understanding and managing full project architectures.

guidelines: |
  - Teach project architecture fundamentals: modular communication, standardization, and structure.
  - Explain module integration within larger systems using examples and case studies.
  - Compare architectural styles, discuss suitability, and share learning resources.
  - Provide practical exercises for real-world application.
  - Analyze multi-layered software (frontend, backend, data storage) to illustrate architecture.
  - Offer educational insights: review README files and source code for comprehension.
  - Use diagrams and visuals to clarify system interactions.
  - Prefer clarity over jargon; use plain, accessible language.
  - Focus on architecture concepts — no coding solutions.
  - Be detailed yet concise; avoid information overload.
  - Include real-world examples for practical relevance.
  - Ask clarifying questions about unclear project details.
  - Maintain a professional, approachable tone.
  - Use everyday analogies for complex concepts.

style: |
  Clear, didactic, structured.
  Encourage understanding of architecture as a living system, not just code components.
"""
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Observation: The output quality didn’t just stay the same — it improved. ChatGPT understood the intent better, and responses became more focused.

Experiment Code Example 

# Universal adapter
# using [llm-api-adapter](https://github.com/Inozem/llm_api_adapter)
# makes it easy to switch between different providers for testing
# can be installed easily via: pip install llm-api-adapter
from llm_api_adapter.universal_adapter import UniversalLLMAPIAdapter

messages_a = [
    {"role": "system", "content": prompt_a},
    {"role": "system", "content": "Help me to create weather application."},
]
messages_b = [
    {"role": "system", "content": prompt_b},
    {"role": "system", "content": "Help me to create weather application."},
]
adapter = UniversalLLMAPIAdapter(
    organization="openai",
    model="gpt-5-nano",
    api_key=openai_api_key,
)
# Runs
resp_a = adapter.chat(messages=messages_a)
resp_b = adapter.chat(messages=messages_b)
# Token savings
tokens_a = resp_a.usage.input_tokens
tokens_b = resp_b.usage.input_tokens
saved = tokens_a - tokens_b
rel = (saved / tokens_a) * 100 if tokens_a else 0
print("PROMPT A tokens:", tokens_a)
print("PROMPT B tokens:", tokens_b)
print("Saved tokens:", saved)
print(f"Relative saving: {rel:.1f}%")
# Cost
print("Cost A:", f"{resp_a.cost_input:.8f} {resp_a.currency}")
print("Cost B:", f"{resp_b.cost_input:.8f} {resp_b.currency}")
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Prompt I used to generate the new YAML-formatted version:

Optimize this prompt into YAML format
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Conclusion: Structured prompts are not just cleaner — they’re cheaper. Try YAML structuring in your next LLM project. It’s simple, reproducible, and can cut your costs by ~30%.

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