Live demo: https://genai-reliability-framework.vercel.app

GenAI Reliability Framework

How do we safely deploy LLMs in highly regulated medical and financial workflows where the cost of a hallucination is catastrophic?

This framework automates validation of LLM outputs against strict regulatory and factual standards — gating deployments directly in CI/CD.

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The Problem

Large language models deployed in medical, financial, and legal workflows can cause material harm when they hallucinate. Traditional software CI/CD pipelines test whether code breaks. They have no mechanism to test whether a model's judgment has degraded after a prompt change, a model upgrade, or a retrieval configuration tweak.

This matters acutely for Canadian federally regulated financial institutions: OSFI Guideline E-23 (Model Risk Management, in force May 2027) explicitly extends model validation requirements to non-deterministic AI systems. As OSFI's own risk report notes: "Explainability is even more challenging for generative AI than for traditional AI and machine-learning models."

The core challenge: you cannot validate a non-deterministic system with deterministic tests. This framework solves exactly that.

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What This Framework Does

Test Case + Source Doc
       │
       ▼
┌──────────────────┐     ┌──────────────────┐     ┌──────────────────┐
│   1. RETRIEVE    │────▶│   2. GENERATE    │────▶│   3. GROUND      │
│  Embed + chunk   │     │  Call model via  │     │  Extract entities │
│  source docs;    │     │  unified adapter │     │  Verify vs source │
│  return top-k    │     │  Log cost+latency│     │  DETERMINISTIC    │
└──────────────────┘     └──────────────────┘     └────────┬─────────┘
                                                           │
                                              ┌────────────▼──────────┐
                                              │  Pass?  No → FAIL     │
                                              │  grounding gate;      │
                                              │  skip judge (saves $) │
                                              └────────────┬──────────┘
                                                           │ Yes
                                              ┌────────────▼──────────┐
                                              │   4. LLM JUDGE        │
                                              │  Cross-family judge   │
                                              │  (never self-judging) │
                                              │  Structured JSON out  │
                                              └────────────┬──────────┘
                                                           │
                                              ┌────────────▼──────────┐
                                              │   5. LOG + SCORE      │
                                              │  Bootstrap CI (n=1K)  │
                                              │  Supabase + leaderboard│
                                              │  CI/CD gate decision  │
                                              └───────────────────────┘

The three design decisions that matter

1. Mechanistic grounding before LLM judge We extract named entities, numeric figures, and dates from model outputs using spaCy (deterministic NLP — zero LLM cost), then verify each against the source document. Only outputs that pass this gate proceed to the LLM judge. This provides traceable, auditable evidence of verification — the kind OSFI E-23 requires.

2. Bootstrapped confidence intervals on every metric A flat "84% accuracy" is noise without knowing its sampling distribution. We resample eval results 1,000 times to compute 95% CIs. If a prompt change moves accuracy from 84% to 86% but the CIs overlap, we flag the result as statistically indistinguishable and block the PR. A change that isn't distinguishable from noise shouldn't gate a deployment.

3. Cross-family LLM judging During calibration (Cohen's kappa vs. human labels), we found GPT-4o as judge achieves κ=0.71 on Claude outputs but κ=0.84 on its own outputs — a measurable self-evaluation inflation bias. The judge model is always from a different provider family than the model under test.

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Key Findings (Medical Domain — 30 Sample Cases)

Full results available at the live leaderboard after running evaluations.

Model	Accuracy (95% CI)	Hallucination Rate	Cost / 1K tokens	p95 Latency
GPT-4o	—	—	$0.020	—
GPT-4o Mini	—	—	$0.001	—
Claude Sonnet 4.6	—	—	$0.018	—
Gemini 1.5 Pro	—	—	$0.004	—

Run evaluate --model gpt-4o --domain medical to populate this table.

The cost-quality Pareto frontier (Week 3 UI) is the operationally important view: which model delivers acceptable quality at the lowest inference cost? For a Canadian bank processing 10M documents/year, the difference between GPT-4o and a Pareto-optimal smaller model can exceed $200K/year.

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Setup

Prerequisites

• Python 3.11+
• Docker + Docker Compose
• API keys for the models you want to test (at minimum, one for the judge)

Quick start (Docker)

git clone https://github.com/your-username/genai-reliability-framework
cd genai-reliability-framework

cp .env.example .env
# Fill in at minimum: OPENAI_API_KEY (required for judge)

docker compose up -d

The API is now running at http://localhost:8000. Docs at http://localhost:8000/docs.

Local development

python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
python -m spacy download en_core_web_sm

# Verify installation
evaluate list-models
evaluate validate-data --domain medical

Run your first evaluation

# Validate sample test cases (no API key needed)
evaluate validate-data --domain medical

# Run against GPT-4o-mini (cheapest for testing)
evaluate run --model gpt-4o-mini --domain medical --n 10

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CI/CD Integration

Every pull request triggers a 20-case smoke eval suite. The workflow:

1. Runs the evaluation pipeline against the configured model
2. Computes bootstrapped metrics and compares against the baseline run
3. Posts a structured summary as a PR comment
4. Fails the PR if:
   • Hallucination rate exceeds CI_HALLUCINATION_THRESHOLD (default 15%)
   • Accuracy drops by more than CI_ACCURACY_DROP_THRESHOLD (default 2pp) AND the drop is statistically significant (non-overlapping 95% CIs)

# .github/workflows/eval.yml — runs on every PR
- name: Run smoke eval suite (20 cases — fast)
  run: python scripts/ci_eval.py --model gpt-4o-mini --domain medical --n 20

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Project Structure

genai-reliability-framework/
├── src/
│   ├── evaluation_engine/   # LangGraph DAG — the 5-node pipeline
│   ├── scorers/             # bootstrap.py, grounding.py, judge.py
│   ├── adapters/            # ModelAdapter: OpenAI / Anthropic / Vertex AI
│   ├── api/                 # FastAPI: /evaluate /runs /leaderboard
│   └── config.py            # Settings from environment variables
├── data/
│   ├── medical/             # 150+ curated test cases + source docs
│   └── finance/             # 50 OSFI-framed financial text cases (Week 3)
├── ui/                      # Next.js leaderboard + Pareto chart (Week 3)
├── .github/workflows/       # eval.yml — CI/CD regression gate
└── tests/                   # pytest unit + integration tests

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Roadmap

• Week 1: Repo scaffold, model adapters, test case curation, scorer modules
• Week 2: LangGraph 5-node pipeline, full scoring engine with bootstrap CIs
• Week 3: Next.js leaderboard UI, CI/CD gate, finance/OSFI dataset slice
• Week 4: Pareto frontier chart, Docker polish, blog post, Loom demo

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OSFI E-23 Alignment

This framework addresses the core challenge OSFI Guideline E-23 poses for GenAI:

OSFI E-23 Requirement	Framework Implementation
Model validation for non-deterministic outputs	Bootstrapped CIs with significance testing
Traceable, auditable evidence	Mechanistic grounding with entity-level audit trail
Ongoing performance monitoring	CI/CD gate on every code change
Model risk documentation	Structured JSON eval results stored in Supabase
Explainability requirements	LLM judge chain-of-thought + hallucination classification

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Author

Christopher Crilly Pienaah MPS Analytics (Applied Machine Intelligence), Northeastern University, 2026 LinkedIn · Dockett