An enterprise-grade, interactive LLM Response Evaluation & Benchmarking Framework designed to simulate real-world AI Quality Assurance, RLHF annotation, and model evaluation protocols (e.g. Outlier, Scale AI, LMSYS Chatbot Arena).
Built by Pratima Kumari.
Modern Generative AI deployment requires rigorous human-in-the-loop (RLHF) and automated evaluation to ensure reliability, instruction adherence, and safety. This framework provides:
- 120+ Rigorous Benchmark Prompts spanning 6 diverse domains:
- Reasoning & Logic (formal deductions, deduction puzzles, case analysis)
- Math & Algorithmic Problem Solving (combinatorics, modular arithmetic, dynamic programming)
- Code Generation & Debugging (concurrency, topological sorts, memory leaks)
- Factual & Long-form QA (biochemistry, economic history, aerospace engineering)
- Instruction Following & Negative Constraints (exact word count, disallowed vocabulary, pure JSON schema)
- Creative & Technical Writing (HLD design docs, RFC proposals, incident postmortems)
- Multi-Model Generations: Outputs evaluated across Claude 3.5 Sonnet, GPT-4o, Gemini 1.5 Pro, and Llama 3 70B.
- 7-Dimensional Scoring Rubric (1.0 to 5.0 scale):
- 🎯 Accuracy: Factual alignment with ground-truth facts.
- 🎯 Relevance: Topical focus, directness, and avoiding drift.
- 🎯 Completeness: Addressing all primary and secondary prompt requirements.
- 🎯 Instruction Following: Adherence to structural guidelines and negative constraints.
- 🎯 Reasoning Quality: Logical progression, absence of fallacies, valid proof steps.
- 🎯 Clarity & Tone: Readability, appropriate register, clean organization.
⚠️ Hallucination Penalty: Deductions scaled to error severity.
- Bradley-Terry Elo Tournament: Automated pairwise win-rate matrix generating dynamic Elo rankings.
- Error Taxonomy Classification: Tracks failures including Constraint Violation, Factual Hallucination, Logical Fallacy, Incomplete Scope, and Spurious Detail.
- Detailed Evaluation Rationales: Natural language justifications per score.
- Interactive Web Dashboard: Glassmorphism UI with real-time radar charts, filterable prompt explorer, model comparison tabs, and downloadable audit reports.
llm-benchmark-eval/
├── dataset/
│ ├── benchmark_prompts.json # 120 curated multi-model prompt benchmarks
│ └── generate_dataset.py # Dataset generation script
├── reports/
│ └── evaluation_executive_report.md # Generated executive audit report
├── evaluator.py # Python evaluation & Elo rating engine
├── index.html # Interactive Glassmorphism Dashboard
├── style.css # Dark-mode styling and animations
├── app.js # Client-side analytics & Canvas radar chart
└── README.md # Documentation
python evaluator.pyThis runs the pairwise Elo tournament, computes rubric averages, and writes reports/evaluation_executive_report.md.
Serve the dashboard locally:
npx serve . -l 3001
# Or using Python's built-in server:
python -m http.server 3001Navigate to http://localhost:3001 in your browser.
| Dimension | 5 (Excellent) | 3 (Acceptable) | 1 (Failing) |
|---|---|---|---|
| Accuracy | Flawless factual alignment with ground truth. | Minor non-critical inaccuracy. | Blatant factual fabrication. |
| Instruction Following | 100% adherence to all negative and structural rules. | Minor format omission (e.g. extra whitespace). | Violated core negative constraint. |
| Reasoning | Rigorous step-by-step mathematical/logical proof. | Correct final answer but skipped intermediate step. | Logical non-sequitur or circular logic. |
| Completeness | Thoroughly covers all constraints and edge cases. | Answers main prompt, skips secondary question. | Extremely superficial or truncated. |
LLM Evaluation • RLHF Annotation • Model Benchmarking • Elo Tournament • Instruction Following • Error Taxonomy • AI Quality Assurance • Bradley-Terry Model
© 2026 Pratima Kumari. Licensed under MIT.