Science-Backed PromptRefiner™: -Prompting Without Guesswork

Hosted by Silali Banerjee

Fri, May 1, 2026

12:00 AM UTC (30 minutes)

Virtual (Zoom)

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Science-Backed PromptRefiner™: Prompting Without Guesswork
Silali Banerjee
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What you'll learn

Live Demo of PromptRefiner™

See how PromptRefiner™ turns a messy prompt into a clear, usable result.

Move beyond prompt packs and prompt libraries

Move beyond collecting prompts. Learn how to design your own prompts for consistent, decision-quality AI outputs.

Multiply your prompting productivity, no trial and error.

Go beyond tips. Optionally, see the science behind each step in PromptRefiner™.

Why this topic matters

Prompting feels like trial and error: ask, tweak, retry, fix. The problem is not the model, but the lack of structure. LLMs are probabilistic, not random. When guided properly, they can produce consistent, reliable outputs. This workshop shows how to move from guesswork to repeatable results.

You'll learn from

Silali Banerjee

Creator, AIgogy Framework™ | Science-backed PromptRefiner™ | Advisor

Creator, AIgogy Framework™ | Science-backed PromptRefiner™ | Advisor

I focus on an often-overlooked aspect of human–AI communicatiounpredictable. With structured guidance, they can produce mostly deterministic, repeatable results.

My approach draws from physics, software engineering, and teaching—treating AI interaction as a systems and learning problem.

Backed by ongoing research (paper under review), the AIgogy Framework™ replaces trial-and-error prompting with a structured approach to achieving consistent, decision-quality outputs.

Previously at

Rockford University
Leland
Handshake
Per Scholas
CodePath

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