291 Methodology Cf22d359

TRIZ + AI Methodology

How to effectively combine TRIZ with Large Language Models for systematic innovation.

Overview

TRIZ (Theory of Inventive Problem Solving) provides structured frameworks for innovation. LLMs provide vast knowledge and reasoning capabilities. Together, they create a powerful human-AI collaboration for problem-solving.

The TRIZ-AI Synergy Model

                    ┌─────────────────┐
                    │   Human Expert  │
                    │   (Direction)   │
                    └────────┬────────┘
                             │
              ┌──────────────┼──────────────┐
              ▼              ▼              ▼
        ┌─────────┐    ┌─────────┐    ┌─────────┐
        │  TRIZ   │    │   LLM   │    │ Domain  │
        │Framework│    │Knowledge│    │Expertise│
        └────┬────┘    └────┬────┘    └────┬────┘
              │              │              │
              └──────────────┼──────────────┘
                             ▼
                    ┌─────────────────┐
                    │   Innovative    │
                    │    Solutions    │
                    └─────────────────┘

Role Distribution

Prompt Engineering Techniques for TRIZ

1. Chain-of-Thought (CoT) Prompting

What: Ask LLM to reason step-by-step through TRIZ methodology.

Why: TRIZ is inherently sequential. CoT ensures each step is properly completed before moving to the next.

Example:

Let's solve this problem step-by-step using TRIZ:

Step 1: First, identify the main function of the system...
Step 2: Now, what parameter are you trying to improve?
Step 3: When you improve that, what gets worse?
Step 4: Let's map these to TRIZ's 39 parameters...
Step 5: Looking at the contradiction matrix, the suggested principles are...
Step 6: Now let's apply each principle to your specific problem...

Research Finding: TRIZ-GPT found CoT achieved 0.691 recall for contradiction analysis, outperforming basic prompts.

2. Role Prompting

What: Assign a specific TRIZ expert persona to the LLM.

Why: Primes the model to access TRIZ-relevant knowledge and reasoning patterns.

Example Roles:

Template:

You are a TRIZ Master Consultant with 20+ years of experience applying
TRIZ methodology across industries. You have deep knowledge of the
40 Inventive Principles, Contradiction Matrix, ARIZ algorithm, and
Evolution Trends. Guide the user through systematic problem-solving.

3. Few-Shot Prompting

What: Provide 1-3 solved examples before the actual problem.

Why: Shows the expected reasoning pattern and output format.

Example:

Here's how TRIZ contradiction analysis works:

EXAMPLE 1:
Problem: Car needs to be faster but engine gets heavier
Improving: Speed (#9)
Worsening: Weight of moving object (#1)
Matrix suggests: Principles 8, 15, 29, 34

EXAMPLE 2:
Problem: Phone needs longer battery but should be thinner
Improving: Duration of action (#15)
Worsening: Length of moving object (#3)
Matrix suggests: Principles 3, 35, 39, 2

NOW ANALYZE:
Problem: [User's problem]

Research Finding: Few-shot prompting showed superior token efficiency for solution generation in TRIZ-GPT.

4. Retrieval-Augmented Generation (RAG)

What: Supply specific TRIZ knowledge based on the problem context.

Why: LLMs may have general TRIZ knowledge but need precise definitions and matrix data.

What to Retrieve:

Example (AutoTRIZ approach):

Based on your contradiction:
- Improving: #9 Speed
- Worsening: #27 Reliability

The Contradiction Matrix recommends Principles: 21, 35, 11, 28

Here are the detailed descriptions:
[Inject principle 21 description]
[Inject principle 35 description]
...

5. Multi-Agent Collaboration

What: Use multiple specialized AI agents that collaborate on TRIZ problems.

Why: Complex problems benefit from diverse perspectives and specialized tools.

Agent Team Structure (from TRIZ Agents research):

Workflow:

1. Project Manager receives problem
2. Domain Engineers identify system components
3. TRIZ Specialist runs contradiction analysis
4. Multiple agents brainstorm solutions
5. Domain Engineers evaluate feasibility
6. Documentation compiles final report

TRIZ + AI Workflow Models

Model 1: AutoTRIZ (Fully Automated)

User Problem → [LLM: Extract Parameters] → [Lookup: Contradiction Matrix]
→ [LLM: Generate Solutions] → Solution Report

Best for: Quick ideation, users with limited TRIZ knowledge Limitation: May miss nuanced problem aspects

Model 2: TRIZ-GPT (LLM-Augmented)

User Problem → [LLM: Guided Analysis] → [Human: Validate]
→ [LLM: Principle Application] → [Human: Evaluate] → Solutions

Best for: Balanced automation and human judgment Advantage: Reduces cognitive load while maintaining quality control

Model 3: Human-Led with AI Support

Human Analysis → [AI: Knowledge Retrieval] → Human Synthesis
→ [AI: Expand Ideas] → Human Selection → [AI: Detail Solutions]

Best for: Expert users, complex/novel problems Advantage: Maximum control and depth of analysis

Quality Assurance

Validation Checklist

Before accepting AI-generated TRIZ analysis:

• Is the contradiction correctly identified?
• Do parameters accurately map to TRIZ's 39 parameters?
• Are suggested principles from the actual matrix?
• Do generated solutions actually apply the principles?
• Are solutions technically feasible?
• Have multiple principles been explored?

Common AI Errors in TRIZ

Integration with Other Methodologies

TRIZ + Design Thinking

Design Thinking: Empathize → Define → Ideate → Prototype → Test
                     ↓         ↓        ↓
TRIZ Integration: [User study] [IFR] [40 Principles] [Feasibility] [Validate]

TRIZ + Six Sigma DMAIC

TRIZ + Biomimicry

1. Abstract problem to function
2. Ask: "How does nature solve [function]?"
3. Apply TRIZ principles to biological analog
4. Transfer back to technical domain

Prompt Templates by Use Case

Quick Ideation (5 min)

I have a technical problem: [problem]
What are the top 3 TRIZ principles that might help?
For each, give me one concrete solution idea.

Deep Analysis (30 min)

Act as a TRIZ Master. Guide me through full analysis:
1. Help me define the Ideal Final Result
2. Identify the technical contradiction
3. Map to 39 parameters
4. Find principles from the matrix
5. Generate 5+ solutions per principle
6. Evaluate each solution's feasibility

Innovation Workshop

We're running a TRIZ innovation workshop on [topic].

Phase 1: Help participants identify contradictions in their systems
Phase 2: For each contradiction, provide the matrix principles
Phase 3: Facilitate ideation using the principles
Phase 4: Help evaluate and prioritize solutions
Phase 5: Create an action plan for top 3 ideas

Format each phase as interactive exercises.

Performance Benchmarks

From published research:

Recommendation: Use LLM for breadth and speed, human expert for depth and validation.

Tools and Resources

GitHub Repositories

• jenson500/triz-prompt-engineering - XML prompts (MIT)

Research Papers

• AutoTRIZ (arXiv:2403.13002)
• TRIZ-GPT (arXiv:2408.05897)
• TRIZ Agents (arXiv:2506.18783)

Organizations

• MATRIZ - International TRIZ Association
• ccTOPP - Collaborative TRIZ Prompt Project

Conferences

• TRAI2025 (Paris-Saclay, Nov 2025) - TRIZ + AI Conference

Summary: When to Use AI with TRIZ