TRIZmaxxing is the deliberate use of
Large Language Models (LLMs) to amplify TRIZ-based innovation by systematically exploring contradictions, resources, analogies, and evolutionary patterns across many domains in order to discover non-obvious solutions.
In simple terms:
TRIZ tells you where to look; LLMs dramatically increase how much of the solution
space you can search.
An LLM acts as an
idea multiplier and cross-domain knowledge engine, allowing a person to examine
far more potential solution paths than would be practical manually.
Problem: Smartphones overheat under heavy
load.
Traditional approach:
Bigger heat sink
Lower processor performance
Add a fan
TRIZmaxxing approach using an LLM:
Identify the contradiction:
The
phone should be powerful and remain cool.
Search for analogies in other fields:
Biology → sweating and blood circulation.
Data centers → workload migration.
Traffic engineering → load balancing.
Architecture → passive cooling and thermal mass.
Generate concepts:
Move intensive tasks between CPU cores to
spread heat.
Temporarily store heat in phase-
change materials.
Redirect heat toward areas less frequently touched by the user.
Predict thermal spikes and pre-cool the
system before they occur.
The innovation comes not from inventing entirely
new physics, but from transferring proven ideas from other domains into the smartphone problem.
Formula
TRIZmaxxing = TRIZ × LLM
Where:
TRIZ provides the structure of inventive thinking.
LLM provides large-scale cross-domain search and rapid hypothesis generation.
