A timeless guide to thinking and building like a prompt engineer. This cheatsheet focuses on core principles and patterns that apply across any model, provider, or tool — whether you're using OpenAI, Claude, Llama, or something that doesn't exist yet.
This is not a cookbook or quickstart. It's a mindset guide — built for those who want to reason clearly and build reliably with LLMs.
LLMs are probabilistic next-token predictors, not deterministic logic machines. Prompt engineering is about:
- Designing clear, structured inputs
- Working within context and token limits
- Thinking iteratively, not magically
- Debugging failures like a system, not like a mystery
Treat prompts as interfaces, not incantations.
Ask the model to do a task with no examples.
"Summarize the following article in 3 bullet points: ..."Give one or more examples to improve reliability.
Review: "Great product, but shipping was late."
Response: "Thanks for your feedback! Sorry about the delay..."
Review: "Terrible quality."
Response: "We're sorry to hear that. Could you share more details so we can improve?"Set a role for the model to adopt.
System: You are a technical support agent who speaks clearly and concisely.
User: My internet keeps cutting out. What should I do?Ask for output formats explicitly.
"List the steps as JSON: [step1, step2, step3]"Controls randomness. Range: 0.0 to 2.0.
0.0→ deterministic, repeatable0.2 – 0.5→ reliable tasks (Ng recommendation)0.7→ balanced (OpenAI default)1.0+→ creative, less reliable
Use top_p for more granular control of randomness.
Always structure prompts with these components:
- Role – Who is the model?
- Task – What do you want?
- Input – What info do they need?
- Constraints – What form should the output take?
- Examples (optional) – Show what success looks like
System: You are a helpful travel assistant that gives concise city guides.
User: I’m visiting Tokyo for 3 days. Suggest an itinerary with 3 activities per day.
Constraints:
- Format your response as bullet points grouped by day.
- Keep each activity description under 20 words.
Example:
Day 1:
- Visit Meiji Shrine in the morning
- Eat sushi at Tsukiji Market
- Explore Shibuya Crossing at night- Be aware of token limits (e.g. 4k, 8k, 128k)
- Use summarization for long chat histories
- Drop irrelevant history when possible
- Explicit > implicit — don't assume the model remembers everything
- Fewer tokens = faster responses and lower cost
- Use tools like OpenAI Tokenizer to inspect prompt size
The best results come when you treat LLMs as tools that augment your thinking, not replace it.
AI thrives when the task is something a human could do in a second or two — things like renaming files, summarizing short content, or generating scaffolding. Don’t force it to solve problems that are too vague or complex. Break hard problems into small, clear ones. That’s when AI shines.
This is aligned with Andrew Ng’s “One-Second Rule” — tasks that a human can perform in under one second are great candidates for automation.
When you evaluate a model's performance, accuracy is key — but perfect accuracy is not realistic. Ambiguity, nuance, and subjectivity are baked into language. Instead of aiming for 100%, aim for consistent and explainable behavior, and iterate. Treat errors as feedback loops, not failures.
(In practice, many real-world tasks operate safely with 70–90% accuracy — just make sure you know your risk tolerance.)
Andrew Ng emphasizes setting high but achievable accuracy standards, and treating improvement as an ongoing process.
Don't outsource your learning. Let the LLM guide, question, and collaborate — not do everything for you. Ask it for scaffolding, instructions, or alternatives, then build it yourself. That way, you stay in control and deepen your understanding. If you can't maintain the code later, you're not really building.
Experts recommend a human-in-the-loop mindset where you learn with the AI, not through it.
LLM output is fuzzy. Define quality like this:
- Does it meet the task objective?
- Is the output formatted correctly?
- Would a human say it's reasonable?
- Can you detect regressions with A/B comparisons?
LLM success isn’t about perfection — it’s about clarity, consistency, and feedback loops.
Reliable > perfect. Iterate like a product, not like a test.
| Symptom | Likely Cause |
|---|---|
| Hallucination | Vague or underspecified prompts |
| Repetition | Poor constraint or unclear output format |
| Refusal | Misalignment between task and role |
| Loss of context | Too much history or poor summarization |
- ❓ Is the task clearly stated?
- 🧩 Are you using the right prompting pattern?
- 🧠 Is there a clear role and structure?
- 🧱 Could the context window be too full?
- 📣 Try asking the model: “Why did you respond this way?”
Some models support function calling or structured tool use — great for API responses or JSON output.
Example: OpenAI’s
function_callorresponse_format="json"
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ.get("OPENAI_API_KEY"),
)
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": "You are a concise technical writer."},
{
"role": "user",
"content": "Explain what a vector database is in simple terms.",
},
],
temperature=0.3, # Lower = more deterministic
)
print(response.choices[0].message.content)This guide helps you stay grounded when everything else is changing. Focus on clarity. Prompt with intent. And always think like an engineer.