Stacks / 7 workflow stages
Evaluate and Harden an AI System
Turn subjective model quality into repeatable tests, traces, adversarial checks, and a release gate.
Best for
Teams moving an AI feature from 'it seems to work' toward measurable reliability and safer production behavior.
Outcome
A practical evaluation loop covering task quality, regressions, tracing, retrieval quality where relevant, and adversarial/security testing.
Not for
Early ideation where the product task itself is still undefined and there is nothing stable enough to evaluate.
Use this as a decision framework, not a mandatory shopping list. Swap or skip layers based on your existing stack, constraints, and risk profile.
1. Define the eval set
OpenAI Evaluation Flywheel
Start by turning real product behavior and failures into a repeatable evaluation loop.
AlternativesHamel Husain: It's Hard to Eval Is a Product Smell
2. Run regression tests
Braintrust
Track task-level quality across prompts, models, and application changes instead of comparing outputs by feel.
4. Trace failures
Arize Phoenix
Inspect traces and model behavior so a failed evaluation can be diagnosed rather than merely counted.
5. Red-team the application
NVIDIA garak
Probe common LLM vulnerabilities and failure modes with a purpose-built red-team tool.
Alternativesgarak Website
6. Threat-model systematically
MITRE ATLAS
Use a structured adversarial framework to reason about attacks beyond individual prompt-injection examples.
AlternativesOWASP GenAI / LLM Top 10 — 2026
7. Make release criteria explicit
Anthropic Security Guidance
Pair quality metrics with concrete security and deployment considerations before exposing the system more broadly.
AlternativesOWASP GenAI Security Project
Need this workflow built and running, not just linked? EE Solutions implements agentic stacks like this one. EE Solutions is a senior technology team for private capital firms and their portfolio companies.
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