Speak to AI
Like a Wizard

Role Assignment

Primes the LLM with expert personas — "You are a senior UX designer at FAANG with 15 years of experience..." — dramatically improving output quality.

Chain-of-Thought

Instructs the model to reason step by step before concluding, unlocking deeper analysis and reducing hallucinations on complex tasks.

Output Formatting

Specifies exact output structures — headers, tables, JSON, bullet trees — so you get machine-readable or human-ready results on demand.

Few-Shot Examples

Embeds concrete examples of good vs bad outputs directly in the prompt, calibrating the AI's understanding of exactly what you want.

Constraint Injection

Adds crisp boundaries — word limits, avoid-lists, audience specs — that prevent the model from drifting into generic unhelpful territory.

LLM-Specific Tuning

Optimizes phrasing for Claude, ChatGPT, Gemini, or Llama — each model has quirks and preferred patterns that affect output quality.