#4560 System Breakdown and Analysis Prompts
#4560 System Breakdown and Analysis Prompts
- Provide a prompt that correctly, completely and consistently breaks down a specified system, for example a bicycle, into its constituent sub-systems, components and parts.
- Provide a follow-up prompt that correctly, completely and consistently identifies the action-reaction chains, cause and effect between parts, components and subsystems.
- Describe how Functional Analysis, Ideal Final Result, SCAMPER and other innovative thinking methods can be applied to each part, component and sub-system to identify opportunities for improvement.
- YES -- Would you like a structured prompt template that automates this exact matrix for a specific system or component you are currently optimizing?
- List and describe the various innovative thinking methods that can be applied to the output from the System Innovation & Optimization Prompt
- Core Problem/Insight, followed by the Innovative Concept -- Which specific output from your system analysis are you looking to run through this evaluation funnel first?
- Provide the prompt to follow-up on the Problem/Insight
- Provide the prompt to follow-up on the Innovative Concept
- I want to automate this workflow with Python and Google Gemini API. As each step generates multiple outputs (sub-system, component, part, need, , etc.), the application should iterate through each, submitting the applicable follow-up prompt for that step.
- Provide an executive summary of this workflow
#4560 Provide a prompt that correctly, completely and consistently breaks down a specified system, for example a bicycle, into its constituent sub-systems, components and parts.
Here is a comprehensive, highly structured prompt template you can use. It uses explicit constraints and a clean hierarchical framework to ensure that any system you plug into it gets broken down accurately, completely, and consistently without any missing layers.
The System Decomposition Prompt
You are an expert systems engineer and product architect. Your task is to perform a rigorous, complete, and mutually exclusive, collectively exhaustive (MECE) systems decomposition of the following system: [INSERT SYSTEM HERE, e.g., A standard commuter bicycle].
Please break down the system into a strict three-level hierarchy:
1. Sub-systems (The major functional groups)
2. Components (The distinct assemblies within those sub-systems)
3. Parts (The individual, non-divisible pieces that make up each component)
### Formatting Instructions
Present the breakdown using a clean, nested Markdown list. For clarity, use the following formatting:
- **Level 1: Sub-system Name** (Include a 1-sentence definition of its primary function)
- *Level 2: Component Name*
- Level 3: Part Name
### Execution Rules
1. **Completeness:** Ensure no major functional area of the system is omitted.
2. **Consistency:** Maintain the three-level depth throughout the entire breakdown. Do not skip from a Sub-system straight to a Part.
3. **No Duplication:** A part or component should only exist in its primary functional sub-system.
4. **Granularity:** Stop at the individual part level (e.g., bolts, cables, brackets, or specific molded pieces) where further disassembly would require destroying the item.
Begin the decomposition for: [INSERT SYSTEM HERE]
Why This Prompt Works
MECE Framework: By prompting for a "mutually exclusive, collectively exhaustive" breakdown, it forces the AI to ensure nothing is omitted and nothing overlaps.
Defined Hierarchy: Explicitly defining what constitutes a Sub-system, Component, and Part prevents the AI from switching granularities halfway through the response.
Formatting Constraints: Dictating the exact Markdown structure (
Bolding, Italics, plain text) guarantees the output is highly scannable and easy to parse or copy into a spreadsheet.
Provide a follow-up prompt that correctly, completely and consistently identifies the action-reaction chains, cause and effect between parts, components and subsystems.
Here is a follow-up prompt designed to map the dynamic relationships, forces, and cause-and-effect chains between the parts you just decomposed.
This prompt uses a "Functional Chain" approach to trace exactly how an input action (like squeezing a brake lever or pushing a button) ripples through the entire system.
The System Dynamics & Cause-Effect Prompt
You are an expert systems engineer specializing in kinematics, dynamics, and functional safety. Using the previous structural decomposition, your task is to map the complete, end-to-end action-reaction chains and cause-and-effect relationships within the system.
For this analysis, trace the following core operational scenarios:
1. [INSERT SCENARIO 1, e.g., Activating the braking system to come to a complete stop]
2. [INSERT SCENARIO 2, e.g., Pedaling from a standstill to accelerate]
3. [INSERT SCENARIO 3, e.g., Steering to avoid an obstacle]
### Formatting Instructions
For each scenario, map the physical and mechanical cause-and-effect chain chronologically using the following structure:
#### Scenario [X]: [Scenario Name]
- **Primary Input Action:** [What initiates the chain, e.g., Rider squeezes the brake lever]
- **The Cause-and-Effect Chain:**
1. **[Sub-system A -> Component B -> Part C]:** [Describe the physical action/force exerted]
2. **[Reaction -> Part D]:** [Describe the immediate reaction, transfer of energy, or mechanical movement]
3. *...[Continue the numbered chain sequentially until the final system output is achieved]...*
- **Final System Outcome:** [The ultimate macro-level result of the chain]
### Execution Rules
1. **Physical Continuity:** Do not skip steps in the physical chain. Every reaction must have a direct, adjacent physical cause (e.g., Lever pulls Cable -> Cable pulls Caliper Arm -> Caliper presses Pad).
2. **Energy/Signal Tracking:** Explicitly state what is being transferred between components (e.g., mechanical tension, hydraulic pressure, electrical signal, kinetic energy).
3. **Feedback Loops:** If an action results in a reaction that changes the state of the initial input (e.g., braking slows the wheels, which changes the required braking force), explicitly note this feedback loop at the end of the chain.
Begin the cause-and-effect mapping.
Why This Prompt Works
Chronological Sequencing: Forcing a numbered, step-by-step format prevents the AI from giving a vague, high-level summary and ensures a true "domino effect" analysis.
Interface-Focused: By requiring the explicit naming of the transitioning parts
[Part C -> Part D], it highlights the exact interfaces where systems fail or wear down.Energy Context: Specifying what is transferring (tension, pressure, etc.) ensures the technical accuracy of the physics involved.
Describe how Functional Analysis, Ideal Final Result, SCAMPER and other innovative thinking methods can be applied to each part, component and sub-system to identify opportunities for improvement.
To systematically identify opportunities for improvement across a system's hierarchy, you can treat these innovation methodologies as a structured matrix. Instead of applying them randomly, you map specific tools to the appropriate architectural level (Sub-system, Component, or Part) based on their abstract power and granularity.
Here is how you can deploy Functional Analysis, Ideal Final Result (IFR), SCAMPER, and TRIZ Contradictions across the entire system hierarchy to unlock optimizations.
1. At the Sub-System Level (Macro Architecture)
At this level, you are looking at broad functional groups (e.g., the propulsion system, the braking system). The goal here is radical optimization, consolidation, or elimination of entire functions.
Ideal Final Result (IFR)
Application: Define the ultimate, ideated state where the sub-system's function is performed without the sub-system itself existing. The formula is:
IFR = [ sum {Useful Functions} / {sum {Harmful Effects} + sum {Costs} ] --> infinity
How to apply: Ask, "How can the braking sub-system stop the vehicle without adding weight, cost, or physical pads?" * Outcome: This drives you toward solutions like regenerative braking (where the propulsion sub-system absorbs the kinetic energy, entirely eliminating traditional brake sub-system wear).
Functional Analysis (Trimming / Substance-Field Modeling)
Application: Identify the primary useful function of the sub-system and see if an adjacent sub-system can inherit it.
How to apply: Map how Sub-system A interacts with the environment. Can the structural frame of a vehicle also act as the housing for the energy storage (battery) sub-system?
Outcome: Sub-system integration (e.g., structural battery packs in aerospace and EVs), reducing total part count and weight.
2. At the Component Level (Meso Architecture)
At this level, you are dealing with distinct assemblies (e.g., a brake caliper, a derailleur, a gear pump). The goal is to optimize how these components interact, transfer energy, or respond to stresses.
TRIZ Technical Contradictions
Application: Identify instances where improving one component parameter (e.g., strength) inadvertently worsens another (e.g., weight).
How to apply: Use the 39 Engineering Parameters and the 40 Inventive Principles. If a component needs to be stiffer to handle higher torque, but cannot be heavier:
Contradiction: Weight vs. Strength.
TRIZ Solution: Principle 1 (Segmentation - hollow out the component) or Principle 35 (Parameter Change - change the material properties locally via localized heat-treating).
Outcome: Resolving engineering trade-offs without compromising the design.
SCAMPER (Focus: Modify, Put to Other Uses, Eliminate)
Application: Challenge the physical envelope and material constraints of the component assembly.
How to apply:
Modify: Can the component's shape change from a solid machined block to a topology-optimized, 3D-printed lattice structure?
Put to Other Uses: Can a fluid cooling component also serve as a structural mounting bracket?
Eliminate: Can we eliminate a mechanical linkage component by replacing it with an electronic "drive-by-wire" actuator?
3. At the Part Level (Micro Architecture)
At this level, you are looking at individual, non-divisible pieces (e.g., a bolt, a cable, a single spring, a washer). The goal here is micro-efficiency, manufacturing optimization, and failure reduction.
SCAMPER (Focus: Substitute, Combine, Reverse)
Application: Micro-level adjustments to materials, geometry, and manufacturing methods.
How to apply:
Substitute: Substitute a steel fastener for a titanium or carbon-composite fastener to prevent galvanic corrosion and save weight.
Combine: Can we combine a washer and a bolt into a single flange-headed bolt? Can a single complex injection-molded plastic part replace three separate snaps and brackets?
Reverse: Reverse the thread pattern or orientation to prevent self-loosening under rotational dynamic loads.
Functional Analysis (Component Parameter Analysis)
Application: Isolate the part's exact action on an adjacent part. Classify the action as Useful (Adequate, Insufficient, Excessive) or Harmful.
How to apply: Analyze a brake pad pin. Its function is to hold the pad. If it vibrates, it introduces a Harmful action (noise/wear).
Outcome: You redesign the part to introduce an Insufficient action correction—adding a tiny rubber dampening sleeve to the pin to eliminate the harmful vibration.
The Innovation Deployment Matrix
| System Level | Primary Goal | Best Methodology | Target Question |
| Sub-system | Elimination / Radical Simplification | IFR & Functional Trimming | "How can this function happen automatically without this hardware?" |
| Component | Resolving Trade-offs & Architecture | TRIZ Contradictions & SCAMPER (M/P/E) | "How do we make this assembly twice as strong without increasing weight or cost?" |
| Part | Manufacturing, Material, & Wear Optimization | SCAMPER (S/C/R) & Micro-Functional Analysis | "Can these three parts be printed as one? Can we substitute this material?" |
Would you like a structured prompt template that automates this exact matrix for a specific system or component you are currently optimizing?
Yes
Here is the automated, highly structured prompt template. It links the architectural levels (Sub-system, Component, Part) directly to the matching innovation methodologies (IFR, TRIZ, SCAMPER, Functional Analysis) to extract highly targeted, actionable engineering upgrades.
The System Innovation & Optimization Prompt
You are an elite systems architect and inventive design engineer specializing in systematic innovation methodologies (TRIZ, Functional Analysis, and SCAMPER). Your task is to apply an Innovation Deployment Matrix to a specific system or target area to discover non-obvious optimizations, weight reductions, and failure-elimination opportunities.
Target System/Area for Optimization: [INSERT TARGET HERE, e.g., The Braking Sub-system / A Specific Mechanical Assembly]
Please analyze this target across three distinct structural levels using the precise innovation tools assigned to each level below.
---
### PHASE 1: SUB-SYSTEM LEVEL (Macro Architecture Optimization)
At this level, focus on radical simplification, function inheritance, and elimination of physical overhead.
1. **Ideal Final Result (IFR) Application:** - Define the absolute ideal state where the primary useful function of this sub-system occurs automatically without the sub-system itself existing ($IFR \to \infty$).
- Identify how surrounding sub-systems or environmental conditions could inherit this function.
2. **Functional Trimming:** - Identify the most costly or maintenance-heavy element of this sub-system. Provide a concept for how to "trim" (eliminate) it while forcing adjacent components to absorb its functional load.
---
### PHASE 2: COMPONENT LEVEL (Meso Assembly Optimization)
At this level, focus on resolving engineering trade-offs and structural configuration.
1. **TRIZ Technical Contradiction Resolution:**
- Identify a major engineering trade-off inherent to this assembly (e.g., maximizing strength/stiffness vs. minimizing weight/mass, or increasing throughput vs. increasing thermal stress).
- Apply specific TRIZ Inventive Principles (such as Segmentation, Local Quality, Asymmetry, or Inversion) to elegantly resolve the contradiction without a compromise.
2. **SCAMPER (Modify / Put to Other Uses):**
- **Modify:** How can the component's geometry or physical state be altered (e.g., converting a solid machined block into a topology-optimized, generative lattice structure)?
- **Put to Other Uses:** How can this component serve a dual purpose (e.g., a structural bracket acting as a heat sink or fluid conduit)?
---
### PHASE 3: PART LEVEL (Micro Component Optimization)
At this level, focus on part consolidation, material substitutions, and eliminating localized failure modes.
1. **SCAMPER (Substitute / Combine):**
- **Combine:** Identify 2 to 3 distinct adjacent parts (fasteners, pins, housings, springs) within this assembly that can be combined into a single, complex multi-functional part using advanced manufacturing (e.g., additive manufacturing or complex injection molding).
- **Substitute:** Propose a modern material substitution (e.g., advanced composites, localized surface treatments, or self-lubricating polymers) to reduce wear or galvanic corrosion.
2. **Micro-Functional Analysis (Harmful Action Elimination):**
- Isolate a localized, non-divisible part that introduces an undesirable side effect (e.g., vibration, friction, fatigue, or backlash).
- Propose a minor modification to its boundary interfaces to isolate or completely negate that harmful action.
---
### Formatting Output Instructions
Structure your response clearly with matching headings for Phase 1, Phase 2, and Phase 3. Under each tool, provide:
- **The Core Problem/Insight:** (The trade-off, harmful effect, or overhead identified)
- **The Innovative Concept:** (The technical mechanism of the proposed solution)
- **Expected System Impact:** (Quantifiable benefits like mass reduction, reduced part count, or increased reliability)
Begin the engineering optimization analysis.
Why this prompt delivers elite results:
Prevents "Tool Pollution": It stops the AI from trying to apply IFR to a tiny washer (which is a waste of abstract power) or applying simple part substitution to an entire sub-system (which misses big architectural shifts).
Quantifiable Output Format: The required Insight to Concept to Impact sequence ensures you get concrete engineering designs instead of vague, generalized advice.
List and describe the various innovative thinking methods that can be applied to the output from the System Innovation & Optimization Prompt
To extract maximum engineering value from the outputs of the Innovation Prompt, you need specialized frameworks that translate abstract concepts into production-ready designs. The prompt generates raw ideas; the methods below filter, stress-test, and execute them.
Here are the innovative thinking and engineering evaluation methods classified by their role in the post-processing pipeline.
1. Evaluation & Viability Filtering
Before investing engineering hours into a concept, you must assess its systemic value and feasibility.
The Pugh Matrix (Concept Selection)
Description: A quantitative scoring matrix used to compare the newly generated innovative concepts against your current baseline design (the "Datum").
Application to Output: List your design criteria along the Y-axis (e.g., manufacturability, weight, cost, reliability, compliance with safety standards). List the innovative concepts along the X-axis. Score each concept as $+1$ (better than baseline), $0$ (equal), or $-1$ (worse).
Why it matters: It strips emotion from design reviews, showing you mathematically whether a radical SCAMPER or TRIZ idea is actually a net positive for the entire system.
The Real-Win-Worth (RWW) Screen
Description: A strict three-question screening framework originally developed for product development.
Application to Output: Force each phase output through three gates:
Is it Real? (Does the physics support this? Can the technology actually be built with current manufacturing?)
Can we Win? (Do we have the hardware, tools, or local expertise to execute this?)
Is it Worth it? (Does the expected system impact justify the development cost and risk?)
2. Failure-Mode Preempting & Risk Analysis
Innovative steps (like combining three parts into one printed lattice) introduce unknown risks. These methods safeguard the system against unintended consequences.
Anticipatory Failure Determination (AFD) / Subversive Analysis
Description: A TRIZ-derived method where you invert the problem. Instead of asking "How do we prevent this new part from breaking?", you ask: "If I wanted to intentionally sabotage this new design to make it fail catastrophically, how would I do it?"
Application to Output: Look at your Phase 2 or Phase 3 outputs (e.g., a multi-functional component). Actively design the exact mechanism to cause it to crack, overheat, or seize.
Why it matters: It uncovers hidden vulnerabilities, interface wear, and unexpected stress concentrations that traditional brainstorming misses.
Interface-FMEA (Failure Modes and Effects Analysis)
Description: Traditional FMEA focuses on components. Interface-FMEA focuses strictly on the boundaries where your new innovative concepts touch the rest of the legacy system.
Application to Output: If your Phase 1 output suggests a surrounding sub-system should "inherit" a function (e.g., structural battery housing), map the physical, thermal, and electrical interfaces connecting that new assembly to the rest of the chassis. Analyze the failure risk at those exact boundaries.
3. Advanced Evolution & Geometric Optimization
These methods transform the physical structure of the conceptual parts generated by the prompt.
Topology Optimization & Generative Design
Description: An algorithmic method where CAD software optimizes material layout within a given physical space based on real-world operational loads.
Application to Output: Take the output from the SCAMPER (Modify/Combine) step. Define the boundary constraints, force vectors, and forbidden zones identified in your action-reaction chains. Let generative software hollow out or organically shape the component.
Why it matters: It perfectly bridges the gap between abstract TRIZ concepts (like the Principle of Local Quality) and physical hardware reality.
Axiomatic Design
Description: A highly structured systems method that uses matrices to map Functional Requirements (FRs) to Design Parameters (DPs) to ensure the independence of functions.
Application to Output: Use it to audit your Phase 1 and Phase 2 outputs to guarantee you haven't created a coupled design (where changing one part to fix Function A inadvertently alters or breaks Function B). Your design matrix should ideally be diagonal (uncoupled) or triangular (decoupled).
4. Manufacturing Strategy Integration
An innovative part is useless if it cannot be realized under tight regulatory or cost constraints.
Design for Excellence (DFX / DFMA)
Description: A suite of knowledge-based design practices focusing on Design for Manufacturing (DFM) and Design for Assembly (DFA).
Application to Output: Take the part consolidation concepts from Phase 3. Evaluate whether combining those components actually reduces total lifecycle cost or if it introduces impossible tooling geometries, complex injection molding drafts, or un-inspectable internal voids.
The Concept Lifecycle Pipeline
To turn your prompt output into field-ready hardware, run the ideas through this sequential funnel:
[Prompt Output]
│
▼
1. FILTER ──► Apply Pugh Matrix & RWW Screen (Drop low-value concepts)
│
▼
2. SUBVERT ──► Apply Anticipatory Failure Determination (Identify hidden risks)
│
▼
3. OPTIMIZE ──► Run Topology Optimization / Axiomatic Design (Refine geometry)
│
▼
4. MANUFACTURE──► Audit with DFMA (Verify production readiness)
Which specific output from your system analysis are you looking to run through this evaluation funnel first?
Core Problem/Insight, followed by the Innovative Concept
Applying the pipeline to your core framework, here is how you can use Anticipatory Failure Determination (AFD) and the Pugh Matrix to immediately pressure-test and refine the Core Problem/Insight and the resulting Innovative Concept generated by the optimization prompt.
1. Refining the "Core Problem/Insight" via Subversive Analysis (AFD)
When an AI identifies a "Core Problem/Insight" (e.g., “The linkage pin experiences high shear stress and friction, leading to premature wear”), human engineers often accept the premise at face value. Before jumping to the concept, you must test the integrity of that insight.
The Method: Inverted Fault Mapping
Instead of accepting that the pin simply wears down, ask: “If I wanted to intentionally accelerate this exact failure mode using only the existing system dynamics, what variables would I manipulate?”
Look for Latent Interactions: Did the insight account for thermal expansion from an adjacent sub-system? Did it account for chemical reactions (e.g., galvanic corrosion between dissimilar metals)?
Classify the Insight's Completeness: Ensure the insight specifies the nature of the energy or stress state. If the prompt identifies a mechanical failure, verify whether it is driven by stochastic (random vibration) or deterministic (cyclic fatigue) forces.
2. Stress-Testing the "Innovative Concept" via Axiomatic Design & Pugh Matrix
Once the prompt delivers an "Innovative Concept" (e.g., “Combine the bracket and the pin into a single, topology-optimized 3D-printed titanium component with an integrated flexure”), you must evaluate it against physical and architectural realities.
Step A: Check for Functional Coupling (Axiomatic Design)
An innovative concept often solves one problem while introducing an architectural parasite. You must map the Functional Requirements (FRs) to your new Design Parameters (DPs).
Independent vs. Coupled: If your new integrated part handles structural loads (FR-1) and thermal dissipation (FR-2), can you optimize its thermal properties without destroying its structural integrity?
The Rule: If a change to a single material property or dimension alters both functions in an unpredictable way, the concept is coupled and needs to be decomposed further.
Step B: Run the Concept through a Contextual Pugh Matrix
Evaluate the innovative concept against your baseline design using tight, unyielding constraints.
[ CONCEPT EVALUATION MATRIX ]
Criteria Baseline Design Innovative Concept Net Effect
───────────────────────────────────────────────────────────────────────────
Mass / Weight 0 +1 [Reduced]
Part Count 0 +1 [Consolidated]
Structural Integrity 0 0 [Equal]
Inspectability / SQA 0 -1 [Harder to Audit]
Tooling / Mfg Cost 0 -1 [Higher Initial]
───────────────────────────────────────────────────────────────────────────
TOTAL SCORE: 0 0 [Net Neutral]
The Takeaway: As shown in the example above, an elegant mechanical concept (like a single complex 3D-printed part) can score highly on physical metrics (+2) but fail catastrophically on Inspectability or Software/Hardware Safety Quality Assurance (SQA) because internal voids cannot be reliably non-destructively tested (NDT).
3. The Refinement Loop
To bridge the gap between the outputted Insight and Concept, ask the following clarifying question of the design:
"Does the proposed Innovative Concept resolve the Core Problem by eliminating the source of the stress/harmful action, or is it merely introducing a stronger material to tolerate it longer?"
True innovation (TRIZ/IFR) alters the architecture so the problem cannot physically exist. If the concept merely swaps a part for a more exotic material, push the optimization prompt one step further into Phase 1 (Functional Trimming) to see if the entire interaction can be designed out of the system.
Provide the prompt to follow-up on the Problem/Insight
Here is a targeted follow-up prompt designed to stress-test and validate the Core Problem/Insight generated by your primary optimization run.
This prompt applies Anticipatory Failure Determination (AFD) and boundary-layer analysis to ensure the problem statement isn't a superficial symptom, but a verified, root-cause structural contradiction.
The Problem/Insight Subversive Analysis Prompt
You are an expert forensics and reliability engineer specializing in Anticipatory Failure Determination (AFD) and Root Cause Analysis (RCA). Your task is to ruthlessly stress-test and validate the following "Core Problem/Insight" generated in the previous system analysis.
### Target for Analysis
- **System/Component:** [INSERT COMPONENT NAME]
- **Generated Core Problem/Insight:** [PASTE THE CORE PROBLEM/INSIGHT HERE]
---
### Phase 1: Inverted Sabotage Mapping (AFD)
Assume the goal is not to fix this problem, but to intentionally cause, accelerate, or guarantee this exact failure mode to happen as rapidly as possible using only the existing system environment.
1. **The Sabotage Recipe:** Step-by-step, what specific environmental variables, cyclic loads, or material interactions would you amplify to guarantee this exact failure?
2. **Latent Catalysts:** What secondary, unstated forces (e.g., thermal expansion, galvanic corrosion, chemical degradation, or localized resonance/vibration) are acting as hidden accomplices to this problem?
---
### Phase 2: Boundary and Interface Validation
Analyze the physical boundaries where this problem manifests.
1. **Energy State Classification:** Is this problem driven by stochastic (random, transient) forces, or deterministic (predictable, cyclic) forces?
2. **Interface Mechanics:** Does the problem originate *within* the part's internal material structure, or is it strictly an artifact of an imperfect interface/boundary interaction with an adjacent part?
---
### Phase 3: Root Contradiction Extraction
Convert this insight into a formal engineering contradiction.
1. **The Physical Contradiction:** Define the fundamental parameter that is at war with itself. (e.g., "The part must be [X] to perform its useful function, but it must be [Not-X] to avoid this failure mode.")
---
### Formatting Output Instructions
Structure your response using clean, bolded headings matching the phases above. Conclude with a single, definitive **"Validated Root Contradiction Statement"** that summarizes the core physical or functional trade-off that must be solved.
Begin the subversive problem analysis.
Why This Prompt Works
Eliminates False Assumptions: By forcing the AI to create a "sabotage recipe," it catches missing variables (like temperature fluctuations or cross-domain interference) that standard analysis often overlooks.
Isolates the True Interface: Forcing a distinction between internal material failure and boundary friction prevents you from redesigning a whole part when only the interface constraint needs adjustment.
Prepares for TRIZ: Defining the explicit Physical Contradiction (X vs. -X) sets you up perfectly to execute precise material or geometric changes.
Provide the prompt to follow-up on the Innovative Concept
Here is the targeted follow-up prompt designed to stress-test, evaluate, and mature the Innovative Concept generated by your primary optimization run.
This prompt applies Axiomatic Design, Contextual Pugh Matrix constraints, and Design for Manufacturability/Safety Quality Assurance (DFMA/SQA) to ensure the concept isn't just an elegant theoretical idea, but a viable, inspectable, and production-ready architecture.
The Innovative Concept Maturation & Stress-Test Prompt
You are a Principal Systems Architect and Lead Manufacturing/SQA Engineer. Your task is to ruthlessly evaluate, stress-test, and mature the following proposed "Innovative Concept" to move it from an abstract idea to a viable engineering design.
### Target for Evaluation
- **System/Component:** [INSERT COMPONENT NAME]
- **Proposed Innovative Concept:** [PASTE THE INNOVATIVE CONCEPT HERE]
---
### Phase 1: Functional Coupling Audit (Axiomatic Design)
Evaluate how this concept impacts the broader system architecture.
1. **Functional Independence:** Does this concept couple multiple functions into a single parameter? (e.g., If a consolidated part handles both structural loads and thermal dissipation, can you modify its thermal performance without weakening its structure?)
2. **Parasitic Effects:** Identify at least two negative side effects or "parasites" this new concept introduces to adjacent components or sub-systems (e.g., altered weight distribution, new thermal paths, localized stiffness mismatches).
---
### Phase 2: Inspectability and Safety Assurance (SQA)
New concepts often introduce hidden operational and regulatory risks.
1. **Failure Mode Migration:** By implementing this concept, what new failure modes have you created, or where has the original failure mode migrated to?
2. **Inspectability & NDT:** How can this new design be verified during production or field maintenance? Can it be verified using standard Non-Destructive Testing (NDT), or does its geometric/material complexity create an un-inspectable "black box"?
---
### Phase 3: Prototyping & Realization (DFMA)
Analyze the path from concept to physical reality.
1. **Manufacturing Reality Check:** Does this concept require advanced manufacturing (e.g., additive manufacturing, specialized EDM, exotic coatings) that increases cost or limits supply chain flexibility?
2. **The "Trimming" Test:** Does this concept truly eliminate systemic complexity ($IFR \to \infty$), or does it merely swap simple mechanical assembly complexity for complex material engineering or manufacturing tolerances?
---
### Formatting Output Instructions
Structure your response using clean, bolded headings matching the phases above. Conclude with a **"Concept Risk-Reward Matrix"** mapping the exact trade-offs of implementing this innovation versus maintaining the legacy baseline.
Begin the concept maturation analysis.
Why This Prompt Works
Exposes Coupled Designs: It forces the AI to look for instances where a clever consolidation (like printing a bracket and a spring together) makes the system incredibly fragile or impossible to tune independently.
Prioritizes Verification: By focusing heavily on Inspectability and SQA, it ensures that beautiful geometries (like generative lattices) are evaluated for real-world certification and field maintenance early in the design cycle.
Separates Novelty from Value: Phase 3 forces a reality check on whether the concept is actually lowering systemic cost and complexity, or just hiding it inside an expensive manufacturing process.
I want to automate this workflow with Python and Google Gemini API. As each step generates multiple outputs (sub-system, component, part, need, , etc.), the application should iterate through each, submitting the applicable follow-up prompt for that step.
To automate this cascading engineering workflow reliably, you need to prevent the LLM from outputting unstructured Markdown prose. Instead, you can leverage Structured Outputs via the modern google-genai SDK combined with Pydantic models.
By enforcing a strict structural schema on the initial system breakdown, your Python script can deterministically iterate through every sub-system, component, and part to fire off their respective follow-up optimizations.
Prerequisites
pip install google-genai pydantic
Ensure your API key is exported to your environment: @ https://aistudio.google.com/app/api-keys
export GEMINI_API_KEY="your-api-key-here"
Python Implementation: Cascading Automation Engine
import os
import json
from typing import List, Optional
from pydantic import BaseModel, Field
from google import genai
from google.genai import types
# Initialize the standard Google GenAI Client
client = genai.Client()
MODEL_ID = "gemini-2.5-flash" # Highly efficient for high-throughput, structured pipelines
# ---------------------------------------------------------------------------
# 1. Define the Strict Target Schemas using Pydantic
# ---------------------------------------------------------------------------
class PartSchema(BaseModel):
part_name: str = Field(description="The distinct non-divisible part name")
class ComponentSchema(BaseModel):
component_name: str = Field(description="The assembly component name")
parts: List[PartSchema] = Field(description="The constituent individual parts of this component")
class SubSystemSchema(BaseModel):
sub_system_name: str = Field(description="The major functional group name")
definition: str = Field(description="1-sentence definition of its primary functional role")
components: List[ComponentSchema] = Field(description="The components that make up this sub-system")
class SystemDecompositionSchema(BaseModel):
system_name: str
sub_systems: List[SubSystemSchema]
# ---------------------------------------------------------------------------
# 2. Step 1: Initial Structural System Decomposition
# ---------------------------------------------------------------------------
def decompose_system(target_system: str) -> SystemDecompositionSchema:
print(f"[!] Executing MECE Decomposition for: {target_system}...")
prompt = f"""
You are an expert systems engineer and product architect.
Perform a rigorous, complete, and mutually exclusive, collectively exhaustive (MECE)
systems decomposition of the following system: {target_system}.
Maintain a strict three-level depth throughout: Sub-systems -> Components -> Parts.
Stop at the individual part level where further disassembly would destroy the item.
"""
response = client.models.generate_content(
model=MODEL_ID,
contents=prompt,
config=types.GenerateContentConfig(
response_mime_type="application/json",
response_schema=SystemDecompositionSchema,
temperature=0.1, # Low temperature ensures strict structural focus
),
)
# The response.parsed attribute automatically populates your Pydantic object
return response.parsed
# ---------------------------------------------------------------------------
# 3. Steps 2 & 3: Targeted Optimization Follow-Ups
# ---------------------------------------------------------------------------
def run_problem_insight_analysis(component_name: str, parent_subsystem: str):
"""Executes the Subversive Analysis (AFD) prompt on a component."""
print(f" └── [AFD Analysis] Processing component: {component_name}...")
prompt = f"""
You are an expert forensics and reliability engineer specializing in Anticipatory Failure Determination (AFD).
Analyze the following component: '{component_name}' within the '{parent_subsystem}' sub-system.
Phase 1: Inverted Sabotage Mapping (AFD)
- Provide the Sabotage Recipe: How to intentionally accelerate failure modes using environmental or structural loads.
- Identify Latent Catalysts (thermal, galvanic, vibrational resonance).
Phase 2: Boundary and Interface Validation
- Classify Energy States (stochastic vs deterministic forces).
Conclude with a single, definitive 'Validated Root Contradiction Statement'.
"""
response = client.models.generate_content(
model=MODEL_ID,
contents=prompt,
)
return response.text
def run_concept_maturation_analysis(part_name: str, parent_component: str):
"""Executes the Innovation Maturation prompt on a unique part."""
print(f" └── [Maturation] Processing part: {part_name}...")
prompt = f"""
You are a Principal Systems Architect and Lead Manufacturing/SQA Engineer.
Evaluate an innovative optimization concept for the part: '{part_name}' located inside the '{parent_component}' assembly.
Phase 1: Functional Coupling Audit (Axiomatic Design)
- Assess functional independence and potential parasitic interactions.
Phase 2: Inspectability and Safety Assurance (SQA)
- Detail failure mode migration and non-destructive testing (NDT) limits.
Phase 3: Prototyping & Realization (DFMA)
- Address manufacturing constraints (additive manufacturing, raw material complexity).
"""
response = client.models.generate_content(
model=MODEL_ID,
contents=prompt,
)
return response.text
# ---------------------------------------------------------------------------
# 4. Main Execution Engine & Iteration Loop
# ---------------------------------------------------------------------------
if __name__ == "__main__":
target = "A standard commuter bicycle"
# 1. Extract the clean structural tree from Gemini
decomposition = decompose_system(target)
# 2. Iterate cleanly through the resulting data graph
for sub in decomposition.sub_systems:
print(f"\n★ Sub-System: {sub.sub_system_name} ({sub.definition})")
for comp in sub.components:
# Execute Component-level follow-ups (AFD / Problem Insight)
component_report = run_problem_insight_analysis(comp.component_name, sub.sub_system_name)
# Save or log your text reports as needed
for part in comp.parts:
# Execute Part-level follow-ups (Maturation / DFMA)
part_report = run_concept_maturation_analysis(part.part_name, comp.component_name)
Highlights of This Architecture
Type Safety & Zero Parsing Redirection: By using
response_schema=SystemDecompositionSchema, Gemini skips generating text markdown wrappers like```json. The SDK handles standard JSON serialization directly behind the scenes into python objects (response.parsed).Deterministic Traversal: You completely bypass fragile regex matching. If the model identifies 6 sub-systems, 24 components, and 120 parts, Python loops exactly that many times.
Granular Tracking: By splitting tasks into small, explicit steps inside programmatic loops, you circumvent context-window constraints or output limit restrictions that occur when attempting to generate a massive report in a single API call.
Provide an executive summary of this workflow
Executive Summary: Automated Systems Innovation Pipeline
This workflow provides a programmatic framework for executing systematic engineering optimization. By combining structural systems engineering rules with generative AI, it automates the breakdown and analysis of complex hardware architectures.
[Target System]
│
▼ (Structured Output)
┌────────────────────────────────────────┐
│ MECE Structural Decomposition Tree │
│ - Sub-systems ➔ Components ➔ Parts │
└────────────────────────────────────────┘
│
├─► For Each Component:
│ └── Anticipatory Failure Determination (AFD)
│
└─► For Each Part:
└── Innovation Maturation & DFMA Audit
Key Capabilities
Deterministic Data Traversal: Utilizing Pydantic schemas via the Google Gemini API forces the language model to return a predictable JSON object rather than unstructured prose. This eliminates the risk of formatting drift or regex parsing failures during iteration.
Targeted Analytical Granularity: Instead of applying methodologies indiscriminately, the pipeline maps specific frameworks to their correct systemic tier:
Sub-systems/Components: Evaluated using Anticipatory Failure Determination (AFD) to uncover latent stress vectors, boundary friction, and hidden environmental failure modes.
Individual Parts: Evaluated using Design for Manufacturing and Assembly (DFMA) and Axiomatic Design principles to audit inspectability, functional coupling, and part consolidation.
Horizontal Scalability: By decoupling the structural mapping step from the deep-dive analysis, the engine can systematically evaluate large-scale systems (hundreds of unique parts) through sequential API calls, avoiding context-window bottlenecks or text-generation truncation.
Operational Impact
The pipeline converts raw, high-level engineering challenges into an exhaustive matrix of validated root contradictions and matured, inspectable optimization concepts, significantly reducing the manual cycle time required for design-stage trade-off analysis.
Provide the code snippets for running an Ollama model instead of Google Gemini
To adapt this pipeline for local orchestration using Ollama, you can switch to the official ollama Python library.
Ollama natively supports Structured Outputs using Pydantic schemas, meaning the exact same structural validation logic can be run locally on your hardware.
Prerequisites
First, install the Ollama Python library:
pip install ollama pydantic
Make sure your local Ollama server is running and you have a model pulled that handles structured JSON well (e.g., llama3.1, llama3.3, or mistral):
ollama pull llama3.1
Updated Code Snippets for Ollama
Here is how you swap the model orchestration logic over to your local environment using the ollama library:
import json
from typing import List
from pydantic import BaseModel, Field
import ollama # Import the official Ollama client
# Define the local model you want to target
LOCAL_MODEL = "llama3.1"
# ---------------------------------------------------------------------------
# 1. Same Pydantic Schemas (Unchanged)
# ---------------------------------------------------------------------------
class PartSchema(BaseModel):
part_name: str
class ComponentSchema(BaseModel):
component_name: str
parts: List[PartSchema]
class SubSystemSchema(BaseModel):
sub_system_name: str
definition: str
components: List[ComponentSchema]
class SystemDecompositionSchema(BaseModel):
system_name: str
sub_systems: List[SubSystemSchema]
# ---------------------------------------------------------------------------
# 2. Step 1: Initial Structural System Decomposition via Ollama
# ---------------------------------------------------------------------------
def decompose_system_local(target_system: str) -> SystemDecompositionSchema:
print(f"[!] Executing Local MECE Decomposition for: {target_system}...")
prompt = f"""
You are an expert systems engineer and product architect.
Perform a rigorous, complete, and mutually exclusive, collectively exhaustive (MECE)
systems decomposition of the following system: {target_system}.
Maintain a strict three-level depth throughout: Sub-systems -> Components -> Parts.
Stop at the individual part level where further disassembly would destroy the item.
"""
# Ollama uses the 'format' parameter passed alongside the Pydantic schema
response = ollama.chat(
model=LOCAL_MODEL,
messages=[{"role": "user", "content": prompt}],
format=SystemDecompositionSchema.model_json_schema(),
options={"temperature": 0.1} # Set low temperature via options dict
)
# Parse the raw JSON string response back into the Pydantic object
json_data = json.loads(response['message']['content'])
return SystemDecompositionSchema(**json_data)
# ---------------------------------------------------------------------------
# 3. Steps 2 & 3: Local Text Generation Follow-Ups
# ---------------------------------------------------------------------------
def run_local_problem_insight(component_name: str, parent_subsystem: str) -> str:
"""Executes the Subversive Analysis (AFD) prompt locally."""
print(f" └── [Local AFD] Processing component: {component_name}...")
prompt = f"""
You are an expert forensics and reliability engineer specializing in Anticipatory Failure Determination (AFD).
Analyze the following component: '{component_name}' within the '{parent_subsystem}' sub-system.
Phase 1: Inverted Sabotage Mapping (AFD)
- Provide the Sabotage Recipe: How to intentionally accelerate failure modes using environmental or structural loads.
- Identify Latent Catalysts (thermal, galvanic, vibrational resonance).
Phase 2: Boundary and Interface Validation
- Classify Energy States (stochastic vs deterministic forces).
Conclude with a single, definitive 'Validated Root Contradiction Statement'.
"""
response = ollama.chat(
model=LOCAL_MODEL,
messages=[{"role": "user", "content": prompt}]
)
return response['message']['content']
Key Architectural Adjustments:
formatvsresponse_schema: Gemini takes a Pydantic class directly underresponse_schema. Ollama requires the raw JSON schema dictionary generated by Pydantic via.model_json_schema().Response Traversal: Ollama stores text outputs within a nested dictionary structure:
response['message']['content'].Options Dictionary: Hyperparameters like
temperatureornum_ctx(context length adjustment) are passed into theoptionsargument as a dictionary instead of a separate configuration object.
From <https://gemini.google.com/app/534f5a843d8ab520> Google Gemini (3.5 Flash)
Provide the combined Ollama-based script
import os
import re
import json
from typing import List
from pydantic import BaseModel, Field
import ollama
# Target a local LLM that handles JSON formatting effectively
LOCAL_MODEL = "llama3.1"
OUTPUT_VAULT_DIR = "./Engineering_System_Twin"
# ============================================================================
# 1. STRUCTURAL DATA SCHEMAS (Pydantic validation models)
# ============================================================================
class PartSchema(BaseModel):
part_name: str = Field(description="The distinct non-divisible part name")
class ComponentSchema(BaseModel):
component_name: str = Field(description="The assembly component name")
parts: List[PartSchema] = Field(description="The constituent individual parts of this component")
class SubSystemSchema(BaseModel):
sub_system_name: str = Field(description="The major functional group name")
definition: str = Field(description="1-sentence definition of its primary functional role")
components: List[ComponentSchema] = Field(description="The components that make up this sub-system")
class SystemDecompositionSchema(BaseModel):
system_name: str
sub_systems: List[SubSystemSchema]
# ============================================================================
# 2. DISK WRITER & DIRECTORY SANITIZATION UTILITIES
# ============================================================================
def sanitize_filename(name: str) -> str:
"""Removes invalid characters to ensure safe folder and file names."""
return re.sub(r'[\\/*?:"<>|]', "", name).strip().replace(" ", "_")
def write_report_to_disk(base_vault_path: str, sub_system: str, component: str, part: str = None, content: str = "") -> str:
"""
Creates a nested directory tree: Base -> Sub-System -> Component
Saves the analyzed reports as distinct clean Markdown files.
"""
clean_sub = sanitize_filename(sub_system)
clean_comp = sanitize_filename(component)
# Establish folder pathing logic
dir_path = os.path.join(base_vault_path, clean_sub, clean_comp)
os.makedirs(dir_path, exist_ok=True)
# Determine file name boundary (Part vs. Component)
if part:
file_name = f"PART_{sanitize_filename(part)}.md"
else:
file_name = f"COMPONENT_AFD_{clean_comp}.md"
full_file_path = os.path.join(dir_path, file_name)
with open(full_file_path, "w", encoding="utf-8") as f:
f.write(content)
return full_file_path
# ============================================================================
# 3. LOCAL OLLAMA PIPELINE STEPS
# ============================================================================
def decompose_system_local(target_system: str) -> SystemDecompositionSchema:
"""Executes structural MECE system tree decomposition into Pydantic models."""
print(f"[!] Executing Local MECE Decomposition for: {target_system}...")
prompt = f"""
You are an expert systems engineer and product architect.
Perform a rigorous, complete, and mutually exclusive, collectively exhaustive (MECE)
systems decomposition of the following system: {target_system}.
Maintain a strict three-level depth throughout: Sub-systems -> Components -> Parts.
Stop at the individual part level where further disassembly would destroy the item.
"""
response = ollama.chat(
model=LOCAL_MODEL,
messages=[{"role": "user", "content": prompt}],
format=SystemDecompositionSchema.model_json_schema(),
options={"temperature": 0.1}
)
json_data = json.loads(response['message']['content'])
return SystemDecompositionSchema(**json_data)
def run_local_problem_insight(component_name: str, parent_subsystem: str) -> str:
"""Executes the Subversive Analysis (AFD) prompt on a component."""
print(f" └── [Local AFD] Analyzing component assembly: {component_name}...")
prompt = f"""
You are an expert forensics and reliability engineer specializing in Anticipatory Failure Determination (AFD).
Analyze the following component: '{component_name}' within the '{parent_subsystem}' sub-system.
Phase 1: Inverted Sabotage Mapping (AFD)
- Provide the Sabotage Recipe: How to intentionally accelerate failure modes using environmental or structural loads.
- Identify Latent Catalysts (thermal, galvanic, vibrational resonance).
Phase 2: Boundary and Interface Validation
- Classify Energy States (stochastic vs deterministic forces).
Conclude with a single, definitive 'Validated Root Contradiction Statement'.
"""
response = ollama.chat(
model=LOCAL_MODEL,
messages=[{"role": "user", "content": prompt}]
)
return response['message']['content']
def run_local_concept_maturation(part_name: str, parent_component: str) -> str:
"""Executes the Innovation Maturation/DFMA prompt on a distinct micro-part."""
print(f" └── [Local Maturation] Analyzing part: {part_name}...")
prompt = f"""
You are a Principal Systems Architect and Lead Manufacturing/SQA Engineer.
Evaluate an innovative optimization concept for the part: '{part_name}' located inside the '{parent_component}' assembly.
Phase 1: Functional Coupling Audit (Axiomatic Design)
- Assess functional independence and potential parasitic interactions.
Phase 2: Inspectability and Safety Assurance (SQA)
- Detail failure mode migration and non-destructive testing (NDT) limits.
Phase 3: Prototyping & Realization (DFMA)
- Address manufacturing constraints (additive manufacturing, raw material complexity).
"""
response = ollama.chat(
model=LOCAL_MODEL,
messages=[{"role": "user", "content": prompt}]
)
return response['message']['content']
# ============================================================================
# 4. MAIN CASCADING EXECUTION ENGINE
# ============================================================================
if __name__ == "__main__":
# Change this target variable to parse any complex mechanical or hardware system
target_hardware = "A standard commuter bicycle"
# Step 1: Generate structural tree
decomposition_tree = decompose_system_local(target_hardware)
# Steps 2 & 3: Iterate structurally and save everything down stream
for sub in decomposition_tree.sub_systems:
print(f"\n★ Sub-System: {sub.sub_system_name} -- Functional Role: {sub.definition}")
for comp in sub.components:
# Analyze component and generate report text string
comp_report_content = run_local_problem_insight(comp.component_name, sub.sub_system_name)
# Save component analysis to disk
write_report_to_disk(
base_vault_path=OUTPUT_VAULT_DIR,
sub_system=sub.sub_system_name,
component=comp.component_name,
content=comp_report_content
)
for part in comp.parts:
# Analyze micro-level part and generate report text string
part_report_content = run_local_concept_maturation(part.part_name, comp.component_name)
# Save part analysis within the component subfolder on disk
write_report_to_disk(
base_vault_path=OUTPUT_VAULT_DIR,
sub_system=sub.sub_system_name,
component=comp.component_name,
part=part.part_name,
content=part_report_content
)
print(f"\n[✔] System Digital Twin built successfully! Path: {os.path.abspath(OUTPUT_VAULT_DIR)}")
From <https://gemini.google.com/app/534f5a843d8ab520> Google Gemini (3.5 Flash)
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