LLM Engineering Cheatsheet

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.

---

Core Philosophy

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.

---

Prompting Patterns (Universal)

Zero-Shot

Ask the model to do a task with no examples.

"Summarize the following article in 3 bullet points: ..."

One-Shot / Few-Shot

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?"

Role-Based Prompting

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?

Constrained Output

Ask for output formats explicitly.

"List the steps as JSON: [step1, step2, step3]"

---

Prompt Structure: The Anatomy

Always structure prompts with these components:

1. Role – Who is the model?
2. Task – What do you want?
3. Input – What info do they need?
4. Constraints – What form should the output take?
5. Examples (optional) – Show what success looks like

Example Prompt (all parts applied)

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

---

Context Management

• 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

---

Evaluation Principles

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?

---

Common Failure Modes

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

---

Recommended Resources

• OpenAI Best Practices
• LangChain Docs
• Ollama for Local Models

---

Minimal Python Example

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.",
        },
    ],
)

print(response.choices[0].message.content)

---

Final Thought

This guide helps you stay grounded when everything else is changing. Focus on clarity. Prompt with intent. And always think like an engineer.