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Cristian Sifuentes
Cristian Sifuentes

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Mastering LLM Prompt Engineering --- The Role, Focus, Boundaries & Context Formula

#ai #llm #promptengineering #productivity

Mastering LLM Prompt Engineering --- The Role, Focus, Boundaries & Context FormulaImage description

Mastering LLM Prompt Engineering --- The Role, Focus, Boundaries & Context Formula

TL;DR --- To get accurate, consistent and valuable responses from
ChatGPT or any other LLM, you need to structure your prompts using
four building blocks: Role, Focus, Boundaries, and
Memory/Context. This transforms vague questions into precise
instructions and boosts the quality of every output.

Why Structure Matters

Large Language Models interpret intent --- not just words. When your
message lacks clarity, they fill in the gaps with assumptions.\
The result: generic responses.\
By contrast, structured prompting makes your intent explicit and
replicable.

1. Define the Role --- "Who is speaking?"

The role gives the model an expert identity. It sets tone,
perspective, and professional context.

Example:\
> Role: B2B Marketing Specialist\
Benefit: Keeps the tone professional and judgment consistent.\
Result: Fewer ambiguities and a coherent communication style.

Pro Tip: Always include the expertise relevant to your task (e.g.,
Senior Data Engineer, Cognitive Psychologist, UX Designer).

2. Define the Focus --- "What should they do?"

The focus defines the action the model should take. It's the
operational core of your prompt.

Example:\
> Focus: Create a LinkedIn post introducing our SaaS product.\
Benefit: Immediate clarity and output relevance.\
Tip: Start with a verb --- summarize, analyze, create,
explain, translate.

3. Define Boundaries --- "How should they do it?"

Boundaries constrain format, tone, and scope so responses stay concise
and consistent.

Example:\

  • Length: no more than 150 characters\
  • Style: clear, colloquial, concrete\
  • Format: bullet points only

Effect: Increased precision, easier readability, and reproducibility
across prompts.

Tip: Boundaries = fewer "AI rambles". They force focus and
structure.

4. Add Memory or Context --- "What is this about?"

The context feeds the LLM background knowledge or conversation
history.\
It tells what the subject is and why it matters.

Example:\
> Product Context: A SaaS that automates accounting processes for small
businesses.\
Impact: More relevant and grounded answers.

Tip: If your last message was vague or missing key details --- fill
them in here.

Putting It All Together --- The Four‑Part Prompt Template 

Role: [who should respond]  
Focus: [the exact task/action]  
Boundaries: [tone, format, length, constraints]  
Context: [background info, goals, product or subject]
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Example:\
> Role: Senior AI educator.\
> Focus: Write a 200‑word summary explaining transformers to
high‑school students.\
> Boundaries: Friendly tone, avoid math, one paragraph.\
> Context: Blog post for an education platform introducing AI concepts.

Result: Coherent, accurate, tone‑appropriate output --- every time.

Advanced Prompting Practices for LLM Experts

Decompose Complex Problems

  • Break down multi‑step reasoning into smaller, verifiable subtasks.\
  • Define each step with its own focus and expected output.

Choose the Right Model & Objective

  • Classification tasks → Smaller, deterministic models.\
  • Creative writing / ideation → Larger, expressive models (e.g., GPT‑4, Claude 3.5).\
  • Accuracy vs. Creativity → Adjust prompt constraints accordingly.

Calibrate Creativity & Precision

  • Too little context → Hallucination risk.\
  • Too much context → Confused or overfit response.\
  • Balance information volume + clarity of task.\
  • Always specify when accuracy matters most.

Learn the Foundations, Not the Tools

LLMs evolve weekly --- but prompt fundamentals remain constant.\
Focus on intent design, clarity, and structured communication.\
With these principles, you'll adapt to any model: Claude, GPT, Gemini,
or open‑source LLMs.

"Artificial intelligence changes every week, but with solid
foundations you can navigate those changes with confidence."\
--- Juan Pablo Rojas, CPO at Platzi

Senior Takeaway

Structured prompting isn't about tricks --- it's about thinking like
an engineer of meaning.\
Define the role, specify the action, limit the scope, and ground it with
context.\
This is how you move from fuzzy chats to repeatable, expert‑level
outputs.

✍️ Written by: Cristian Sifuentes --- AI engineer & LLM strategist,
passionate about practical prompting and knowledge engineering.

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