Bittensor Subnet Concept

Bittensor QualityNet

Decentralized LLM Quality Assurance & Regression Testing Subnet

面向企业的去中心化 LLM 质量保障与回归测试子网

Continuous detection for hallucination, safety risk, quality regression, and cost efficiency through multi-miner evaluation, hidden benchmark validation, and on-chain incentives.

View Architecture Explore MVP
Multi-miner Parallel QA
Hidden Set Reliability Check
Enterprise API LLMOps Ready
QualityNet Architecture live evaluation layer
Enterprise App CI/CD · RAG · Agent Validator Aggregation Engine Hidden Set benchmark Miner Nodes judge · ragas · safety Miner Nodes fact · attack · cost Metrics Report quality delta
91.4 QA Scoreweighted consensus
4.8% Hallucinationregression watch

Problem

Why Web2 LLM Evaluation Is Not Enough

Centralized evaluation platforms can be useful, but enterprise AI reliability needs independent redundancy, transparent aggregation, and scalable adversarial coverage.

01

Single Evaluator Bias

Centralized platforms rely on a small number of judge models, narrowing the evaluation perspective.

02

Black-box Scoring

Users cannot easily verify whether scoring is objective, reproducible, or resistant to hidden model drift.

03

Poor Scalability

Multi-language, multi-task, and industry-specific benchmarks are hard to expand from a closed evaluation stack.

04

Single Point of Failure

Outages, vendor limits, or policy changes can interrupt enterprise AI quality assurance workflows.

QualityNet Solution

A Decentralized Evaluation Layer for Generative AI

QualityNet combines miner competition, model redundancy, hidden validation, cross-evaluation, on-chain incentives, and enterprise APIs into an open QA network for LLMOps infrastructure.

M1

Multi-miner Competition

Miners evaluate the same task with different models, prompts, heuristics, and testing strategies.

R2

Model Redundancy

Multiple judge families reduce single-model preference bias and improve fault tolerance.

H3

Hidden Benchmark Validation

Validators mix hidden benchmark items into real workloads to measure miner reliability.

C4

Cross-validation

Multiple miner outputs are compared to filter abnormal scores, collusion patterns, and low-quality reports.

I5

On-chain Incentives

Reward signals push miners to continuously improve judge accuracy, coverage, and explanation quality.

A6

Enterprise API

Teams connect CI/CD, RAG systems, support agents, and evaluation dashboards through a stable API surface.

MVP Console

Enterprise Quality Dashboard

A control plane for prompt versions, RAG datasets, agent workflows, and regression reports. Values below are simulated for demo presentation.

QualityNet Evaluation Project · RAG Customer Support Version v2.8.1 compared against production baseline · 1,240 evaluation items
24h window Weighted consensus Hidden set passed
Overall Quality Score stable
Hallucination Rate watch
0%
Answer Relevance pass
0%
Faithfulness pass
0%
Task Completion pass
0%
Toxicity Risk low
Low
Cost / Latency ok
0ms
Regression Delta -2.1%
0%

Workflow

How QualityNet Works

Enterprise evaluation jobs move through a subnet loop: structured task creation, validator broadcast, miner execution, reliability filtering, and report delivery.

01

Create Evaluation Project

Enterprises upload prompt, response, context, reference, and task type for a versioned evaluation run.

02

Broadcast Tasks

The console standardizes jobs and broadcasts evaluation tasks to validators through Bittensor RPC.

03

Miner Evaluation

Miners run LLM-as-a-judge, RAGAS, adversarial testing, safety checks, and cost analysis strategies.

04

Validator Aggregation

Validators use hidden samples, cross-validation, and weighted aggregation to screen high-quality reports.

05

Return Report

The dashboard and API return structured metrics, explanations, suggestions, and regression deltas.

Evaluation Projectinput schema
Validator Broadcastsubnet rpc
Miner Competitionparallel judges
Weighted Consensustrust engine
Enterprise Reportapi response

Miner Task Design

Miner Evaluation Tasks

QualityNet tasks use explicit input and output schemas so miners can compete on evaluation quality, explanation depth, safety coverage, and operational efficiency.

input.json 
{
  "prompt": "...",
  "response": "...",
  "context": [
    "doc1",
    "doc2"
  ],
  "reference": "...",
  "task_type": "rag_qa"
}
output.json 
{
  "metrics": {
    "accuracy": 0.85,
    "relevance": 0.90,
    "faithfulness": 0.80,
    "hallucination_rate": 0.10,
    "toxicity": 0.00
  },
  "explanation": "...",
  "suggestions": "..."
}
LLM-as-a-Judge
RAGAS Metrics
Fact Extraction
Prompt Injection Testing
Toxicity Detection
Cost & Latency Analysis
Agent Tool-call Verification

Validator Mechanism

Validator Mechanism

The validator acts as a Trust Engine, blending public tasks, hidden benchmarks, adversarial samples, miner history, and user feedback into a weighted reward signal.

01
Public + Hidden Benchmarks

Known tasks provide transparency while hidden items measure real reliability.

02
Mixed Real Tasks + Adversarial Samples

Validators combine customer-like workloads with targeted failure probes.

03
Trimmed Mean / Weighted Aggregation

Outliers are controlled and reliable miners receive stronger aggregation weight.

04
Miner Reliability Score

Historical accuracy, consistency, latency, and hidden-set performance shape miner trust.

Validator Trust Engine
Hidden Setdelayed reveal
Miner Reportscross-check
Consensusweighted score
Reward Signalon-chain
User Feedbackground truth loop

Business Model

Business Model

QualityNet can start with developer-first API adoption, then expand into enterprise-grade monitoring, private deployment, and premium audit reports.

01

Pay-per-call API

Usage-based evaluation for CI jobs, RAG checks, support bots, and prompt deployments.

02

SaaS Subscription

Hosted dashboard, version history, monitoring alerts, and team controls.

03

Enterprise Private Deployment

Dedicated evaluation gateway for regulated workloads and private benchmark libraries.

04

Premium Audit Reports

Independent reliability audits, regression studies, and model migration assessments.

Early GTM

Open-source SDK LangChain Plugin Free Developer Credits RAG QA Customer Support Enterprise Search

Roadmap

Roadmap

The subnet grows from focused QA and RAG evaluation into broader enterprise AI reliability coverage, then a plugin-based network effect.

0 -> 1

MVP

  • QA and RAG evaluation
  • Basic API
  • Basic dashboard
  • Hidden benchmark validation
1 -> 10

Early Customers

  • Multi-turn conversation
  • Summary evaluation
  • Prompt optimization
  • Version comparison
10 -> 100

Enterprise Expansion

  • Agent tool-call evaluation
  • SQL / code generation evaluation
  • Private deployment
  • Industry-specific benchmarks
100+

Network Effect

  • Real-time monitoring
  • Alerting system
  • Multi-language coverage
  • Third-party metric plugins
  • Evaluation model research

Final Verdict

Why QualityNet Is Worth Building

QualityNet does not generate content. It evaluates the reliability of every generative system built on top of AI. It is the decentralized quality layer for enterprise LLM operations.

  • High-frequency Demand 9/10
  • Automatic Evaluation 8/10
  • Multi-miner Parallel Competition 9/10
  • Enterprise Willingness to Pay 8/10
  • Cheating Cost Control 7/10
  • Bittensor Fit 9/10
  • Differentiation 9/10
  • GTM Feasibility 8/10