Ship LLM Products
That Work. Every Time.
The M.A.G.I. framework for production-grade evaluation: failure taxonomy, CI/CD gates, and continuous improvement.
A framework by Nidhi Vichare
7
I.O.R.M.G.O.D Layers
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M.A.G.I. Pillars
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Eval Metrics
“Evals are not QA.
They're the control plane that keeps production LLM systems
correct, governable, and shippable.”
The thesis behind EvalMaster
Why This Exists
LLMs fail in ways traditional software testing can't see.
A chatbot that answers “Yes, you can expense a coffee machine” when the exclusion policy says otherwise. An agent that calls a tool that doesn't exist and crashes the system. A RAG pipeline that retrieves last year's policy and gives confidently wrong answers. These aren't bugs you find with unit tests.they're failure modes that emerge from how LLMs interact with real data, real tools, and real users
Evals are the discipline of catching these failures before production. You read traces, name the failure modes, build automated checks against each one, and run them as quality gates in CI/CD and production. It's not QA. It's not a dashboard. It's the engineering practice that makes LLM systems trustworthy.
EvalMaster exists because we built this the hard way, and believe every team shipping LLM products deserves a framework that works from day one. M.A.G.I. (Metrics, Automation, Governance, Improvement) is how you operationalize it. I.O.R.M.G.O.D is where it lives in your stack. This site is the complete playbook.
The Impact
From Chaos to Confidence
Before
Manual evaluation with spreadsheets: slow, subjective, never at scale
Failures discovered by users in production, not by gates in CI
Zero visibility into why the model hallucinated or missed context
No ownership: nobody knows whose job evals are
After M.A.G.I.
Automated QAG + G-Eval judges run on every PR in under 5 minutes
Binary quality gates block bad deploys before they ship
Full traces with Langfuse: cost, latency, and quality per query
Clear metric ownership, quarterly reviews, and escalation paths
Capabilities
Built for How LLMs Actually Fail
Four core capabilities that transform evaluation into a first-class engineering practice
Failure Taxonomy, Not Guesswork
Read traces, name failure modes, turn them into automated checks. Your evals test what actually breaks: hallucinations, tool errors, retrieval misses. Not generic accuracy benchmarks.
Learn moreCI/CD Gates in Hours
Pre-built evaluators (QAG, G-Eval, Contextual Precision) with binary pass/fail thresholds. Wire them into your pipeline so bad deploys never ship.
Learn moreFull-Stack Observability
Traces, spans, cost-per-query, and quality A/B, all out of the box with Langfuse. See exactly where your LLM pipeline fails and why.
Learn moreContinuous Improvement Loop
Threshold tuning, golden-dataset versioning, quarterly reviews. Your evals get better as your system evolves, never stale after week one.
Learn moreSee It in Action
The ARROW Production Architecture
Agentic Retrieval & Routing with Observability Workflow. Click to explore full-screen.
The Framework
M.A.G.I.
Four pillars that turn “we should do evals” into a program people can actually run
Metrics
QAG, G-Eval, Contextual Precision & Recall, Tool Correctness. ≤5 metrics per use case. Signal over noise.
ExploreAutomation
Evals run in CI/CD. Offline and online scoring. No manual spreadsheet review standing between you and a deploy.
ExploreGovernance
Metric ownership, quarterly reviews, golden-dataset versioning, clear thresholds with escalation paths.
ExploreImprovement
Full-funnel tracing. Threshold tuning. Open and axial coding on real failures, creating a feedback loop that compounds.
Explore
For Builders
The Reading List
Guides, reference material, and real production failures with diagnoses
What evals actually are
The 5-step loop: look at traces, write notes on failures, group into failure modes, build automated checks, run them in CI and prod. Ships with a failure taxonomy worked end-to-end.
Read the guideLangChain → LangGraph
When to orchestrate vs. chain. Graph, state, nodes, edges.
Read8 Architecture Patterns
From Simple RAG to enterprise multi-agent with full observability.
ReadAgent Tool Hallucination
Schema validation + circuit breakers to stop cascading failures.
ReadRAG Context Failures
When partial evidence creates dangerous answers.
ReadReference Architecture
I.O.R.M.G.O.D
Seven layers. Observability & Eval is load-bearing.
Interface & Gateway
Auth, rate limits, caching
Orchestrator / Agent
Steps, tools, retries, timeouts
Retrieval (RAG)
Vector + keyword, re-ranking
Models
Claude, GPT-4, Gemini, self-hosted
Guardrails
Safety, PII, policy checks
Observability & Eval
load-bearingTraces, cost, quality A/B, judges
Data & Governance
Ingestion, versioning, gold sets
Getting Started
Get Started in 3 Steps
Name Your Failure Modes
Read 40–100 traces. Write one note per failure. Group into 4–7 categories using open and axial coding. This is your failure taxonomy.
30 minBuild Automated Judges
For each failure mode, create a deterministic check or LLM-as-judge evaluator. Set binary pass/fail thresholds. Wire into CI.
1–2 hrsMonitor & Improve
Run evals in production with Langfuse. Sample failures for human review. Tune thresholds quarterly. Your eval program compounds.
OngoingReady to Build Your
Eval Program?
12-week cohort: failure taxonomy, judge calibration, CI gates, production monitoring, cost optimization. Waitlist open for Cohort 1.