Towards AI•2 days ago
The 6 Essential Prompt Engineering Techniques: How to Get 10× Better Results from the Same LLM
Last Updated on February 21, 2026 by Editorial Team Author(s): TANVEER MUSTAFA Originally published on Towards AI. Understanding Zero-Shot, Few-Shot, Chain-of-Thought, Self-Consistency, Tree of Thoughts, and ReAct You ask an LLM to analyze market trends. It gives a vague, generic response. Your colleague asks the same model with a different prompt — receives a detailed, actionable analysis worth consulting fees. Same model, radically different results. The difference? Prompt engineering. Image generated by Author using AIThis article explores six essential prompt engineering techniques, detailing how each technique can drastically improve results when interacting with language models. Techniques discussed include Zero-Shot prompting with clear instructions, Few-Shot prompting learning from examples, Chain-of-Thought for step-by-step reasoning, Self-Consistency through majority voting, Tree of Thoughts to explore multiple reasoning paths, and ReAct which combines reasoning with actions. The author emphasizes how mastering these methods can transform model outputs from vague responses to highly accurate, expert-level analyses. Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI
