Prompt engineering is closer to structured technical writing than to "magic incantations". This article outlines a lightweight framework you can use across LLMs.
The Framework
Define the task, provide reference material, constrain the output format, and give at least one worked example. Then evaluate against a rubric.
Evaluating Prompts
A prompt is only as good as the criteria you evaluate its output against. Use held-out cases and blind ratings whenever possible.
Frequently Asked Questions
TTA ACADEMY is an independent training and career guidance platform — not affiliated with or endorsed by any vendor named on this page. We don't guarantee jobs, approval, or income, and we don't issue certificates. What we do offer:
Articles on this platform are researched, written and reviewed by the TTA ACADEMY team — a group of trainers, mentors, quality specialists and remote-work consultants supporting learners in AI, freelancing, vendor qualification and remote career development.
View Team ProfileReferences & Sources
We link only to official websites, government portals, research papers, official documentation, academic publications, industry reports and vendor documentation.
- Language Models are Few-Shot Learners (GPT-3 paper) — arXiv · Research Paper
- Chain-of-Thought Prompting Elicits Reasoning — arXiv · Research Paper
- OpenAI — Prompt Engineering Guide — OpenAI · Official Documentation
Disclaimer: Information changes over time. TTA ACADEMY is an independent training and career guidance platform — we are not affiliated with or endorsed by any vendor unless explicitly stated. Always verify the latest details directly from the official vendor resources linked on this page.