Implement M.A.G.I. in Your Organization
Follow our proven 4-phase approach to implement production-grade LLM evaluation. From foundations to continuous improvement.
What is M.A.G.I.?
M.A.G.I. is a comprehensive framework for implementing production-grade LLM evaluation. Metrics • Automation • Governance • Improvement
Proprietary Framework: M.A.G.I. is proprietary intellectual property created by Nidhi Vichare. This framework cannot be used without explicit permission. Please contact the author for licensing and usage rights.
M.A.G.I. transforms LLM evaluation from an ad-hoc process into a systematic, production-ready discipline that ensures consistent quality, automated monitoring, and continuous improvement of AI applications at scale.
Metrics
Define and implement core evaluation metrics
Automation
Automate evaluation in CI/CD pipelines
Governance
Establish ownership and review processes
Improvement
Continuous refinement based on data
Why M.A.G.I. Works
Production-Ready
Battle-tested framework used by leading AI companies with proven results in production environments
Fully Automated
CI/CD integration with zero manual evaluation overhead, ensuring consistent quality gates
Continuously Improving
Data-driven refinement and threshold optimization based on real-world performance metrics
Core M.A.G.I. Principles
Business Outcomes First
Tie evaluations to task resolution, CSAT, compliance, and business KPIs
Signal Over Noise
Focus on ≤5 metrics per use case with semantic/judge approaches over n-gram overlap
Gate from Day 0
Implement meaningful thresholds and quality gates from the start of development
Quantitative & Reliable
Human-aligned scores with consistent, measurable evaluation criteria
Instrument Everything
Enable offline computation and comprehensive observability across all components
Continuous Evolution
Regular threshold updates, dataset refresh, and framework improvements based on learnings
Implementation Roadmap
Key Tasks:
- Define use cases and success criteria
- Select core metrics (≤5 per use case)
- Set up logging infrastructure (Langfuse)
- Create golden datasets (50-200 test cases)
Deliverables:
Key Tasks:
- Implement evaluators (LlamaIndex)
- Set up CI/CD gates with initial thresholds
- Run baseline evaluations on current system
- Calibrate thresholds based on results
Deliverables:
Key Tasks:
- Automate evaluation pipeline in CI/CD
- Set up monitoring and alerting
- Implement A/B testing framework
- Train team on evaluation practices
Deliverables:
Key Tasks:
- Quarterly metric reviews and threshold updates
- Golden dataset refresh and expansion
- Team training and knowledge sharing
- Framework evolution based on learnings
Deliverables:
Quick Start Code
pip install llama-index langfuse
npm install langfusefrom langfuse import Langfuse
lf = Langfuse()
trace = lf.trace(name="rag_query")from llama_index.core.evaluation import FaithfulnessEvaluator
evaluator = FaithfulnessEvaluator()# CI/CD pipeline
python eval/run_eval.py --min-faithfulness 0.80