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AI & Machine LearningPrompt Engineering 101: Getting Better Results from LLMs
Published June 1, 2026·Updated October 29, 2026·7 min read
Introduction
Prompt engineering is the practice of structuring inputs to large language models to reliably get the output you want. This guide covers the foundational techniques.
Prerequisites
- Access to any modern LLM (ChatGPT, Claude, Gemini, etc.)
Step-by-Step Guide
Step 1: Be explicit about the task
State the desired format, length, and audience directly instead of implying them.
Step 2: Provide examples (few-shot prompting)
Show the model one or two examples of the input/output pattern you want before asking for a new one.
Step 3: Break complex tasks into steps
Ask the model to reason step by step, or split the task across multiple prompts.
Step 4: Iterate based on failures
Treat prompts like code: test them against edge cases and refine wording that causes inconsistent output.
FAQs
- Does prompt engineering still matter as models improve?
- Yes — clearer instructions and better-structured context consistently improve output quality regardless of model capability.
Summary
Good prompts are explicit, provide relevant context, and are tested like any other interface to a system. Treat prompt design as an iterative process.
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