Prompt Engineering Guide: How to Talk to LLMs

What You'll Learn

Learn how to write effective prompts that get LLMs to produce accurate, useful, and structured responses every time.

Why It Matters

Prompt engineering is the most practical AI skill for developers. A good prompt can make an LLM 10x more useful. A bad prompt wastes time and produces garbage.

Real-World Use

Writing prompts to generate code, summarize documents, extract data from text, and build AI-powered features into your applications.

Fundamental Principles

Be Specific

❌ "Write a function"
✅ "Write a Python function that takes a list of numbers and returns the top 3 largest values, sorting them in descending order"

Provide Context

❌ "Summarize this article"
✅ "Summarize this technical article for a non-technical executive. Focus on business impact and timeline. Max 3 paragraphs."

Define the Output Format

❌ "List some ideas"
✅ "Give me 5 project ideas in a table with columns: Name, Description, Difficulty (1-10), Time to Build"

Techniques

Chain-of-Thought Prompting

Ask the model to reason step by step:

"Solve this problem step by step, then give the final answer.
A store has 120 apples. They sell 40% on Monday and half the remaining on Tuesday. How many are left?"

Few-Shot Prompting

Show examples before asking:

"Classify the sentiment of these reviews:

Review: 'This product is amazing!' → Positive
Review: 'Waste of money, terrible quality' → Negative
Review: 'It's okay, nothing special' → ???"

Role Prompting

Assign a persona:

"Act as a senior Python developer reviewing code. Point out bugs, security issues, and performance problems. Be critical and thorough."

Structured Output

Request JSON, XML, or markdown:

"Extract the following fields from this email and return as JSON: sender, subject, date, urgency (high/medium/low)."

Common Mistakes