Prompt Engineering

The order of few-shot examples in a prompt shifts accuracy by over 40 percentage points. Not luck. Craft. Prompt engineering controls what an AI model outputs, purely through the way you ask. No code, no training, immediately deployable.

The techniques that count

Zero-shot: a single instruction without examples. Works for simple tasks. Few-shot: 2-8 examples in the prompt, improves accuracy by 25-50%. Chain-of-thought: the model thinks step by step. Boosts reasoning results by up to 58%. One variant (self-consistency) raised math accuracy from 18% to 91%.

System prompts define role, constraints, and format. They set the frame in which the model responds.

Prompt, RAG, or fine-tuning?

Prompt engineering changes the input. Fine-tuning changes the model. RAG extends the knowledge. We combine all three, but always start with the prompt. 80% of the results come from the right instruction.

Prompt engineering is the most cost-effective method to adapt an LLM to a task. No weight changes, no training data needed. That is why it is the first lever we apply in every client project.