Prompt Engineering Techniques for Better LLM Outputs
Discover proven prompt engineering techniques that help you get more accurate, structured, and creative responses from large language models like GPT-4, Claude, and LLaMA.
Prompt Engineering Techniques for Better LLM Outputs
Large Language Models (LLMs) like GPT-4, Claude, and LLaMA are incredibly capable — but their performance depends heavily on how you prompt them.
Prompt engineering is the art and science of designing instructions that lead to more accurate, relevant, and creative outputs.
In this guide, we’ll explore key techniques, examples, and frameworks to level up your prompting skills.
🎯 1. Be Explicit and Structured
Ambiguity leads to inconsistent results. Always specify what, how, and format.
❌ Bad Prompt
Tell me about GPT-4.
✅ Better Prompt
Write a 3-paragraph summary explaining GPT-4’s architecture, capabilities, and common use cases. Format each section with a heading.
🧱 2. Use Role Prompting
Assigning a role gives the model context and tone.
Example:
You are a senior data scientist explaining neural networks to beginners. Use simple language and analogies.
This technique helps control style, complexity, and perspective.
🧠 3. Chain of Thought (CoT) Reasoning
Encourage step-by-step thinking to improve logic and reduce errors.
Example:
Let's reason step by step before answering.
For complex reasoning tasks, CoT prompts can increase accuracy dramatically — especially in math, coding, and logic problems.
🧩 4. Few-Shot and Zero-Shot Prompting
- Zero-shot: Model performs a task with no examples.
- Few-shot: Model sees a few examples before answering.
Example (Few-shot Classification):