Learning to write prompts that get exactly what you need from AI. The difference between an average and excellent prompt is a 200–400% difference in output quality.
Prompt engineering is the art and science of crafting instructions (prompts) for AI models that generate precise, useful and consistent outputs. A good prompt is the difference between AI that generates generic text and AI that acts like an expert from your industry.
#BiznesAILab trains in prompt engineering in a business context — not academic. We teach Chain of Thought, Few-shot prompting, Role prompting, Structured Output, System Prompts and advanced patterns for ChatGPT, Claude and Gemini.
AI output quality depends 80% on prompt quality — essential knowledge for every AI user
Standardisation: company prompt library ensures consistent output quality regardless of employee
Savings: a good prompt eliminates 3–5 correction iterations — faster and cheaper
Custom GPTs and AI assistants are only as good as the system prompt defining them
Safety: well-designed prompts reduce risk of hallucinations and incorrect outputs
AI scaling: a company with a prompt library deploys new AI use cases faster and cheaper
How language models work and why certain formulations work better. Principles of tokenisation, context and model limitations.
Role prompting, Zero-shot vs Few-shot, Chain of Thought, Structured Output. Exercises with examples from participants' industries.
System Prompts for Custom GPT, RAG-aware prompting, Multi-step reasoning, output format control (JSON, Markdown, tables).
Creating your own prompt library for the department: templates for sales, HR, marketing, finance, customer service.
How to measure prompt quality, A/B testing prompts, library management and updating prompts when models change.
For everyone who uses AI at work and wants to do it 3× more effectively.
Tell us which AI tools your team uses — we'll tailor the workshop to your tools and industry.