Saabsa · Austin HQ, US delivery
LLM and RAG evaluation before you call it production
Technical buyers do not accept “it seemed good in the demo.” Saabsa builds the evaluation set, the regression check, and the traces that show what the system did. That work is part of the production sprint, and it stays in the build.
A vibe check is not an evaluation framework
LLM evaluation is a set of cases you can rerun: questions the system must answer, questions it must refuse, retrieval hits it must find, and tool calls it must not make. RAG evaluation adds the corpus: the right passage, the wrong tenant’s passage, and the stale policy. AI model evaluation that only reports a public benchmark does not describe your workflow.
AI reliability testing here means those cases run when the prompt, the index, or the model changes. If a change drops a case, the release stops.
Golden tasks
Drawn from real tickets, charts, or documents—not from the prompt author’s imagination alone.
Failure cases
Unanswerable questions, permission boundaries, and the action the agent must escalate.
Regression
The set runs in CI or on a schedule. A pretty demo is not a release gate.
Observability
Traces of retrieval, tool calls, latency, and spend. LLM observability is how you debug Tuesday, not a second product line.
What we will not pretend to measure
We will not quote a leaderboard as proof your assistant works. We will not hide disagreements in an average. The sprint’s ROI note states the baseline and what would falsify it. Ongoing AI cost optimization is a ceiling and a routing rule, the same instinct as ForgeMeter on engineering AI spend: one number finance and the builder can both see.
Evaluation without a system is a spreadsheet. It belongs with production readiness and, when the system is retrieval-heavy, with RAG development. Data pipeline quality is a related discipline; that playbook is DataXPipe, not a renamed eval harness.
Questions buyers ask
Before you book the sprint
Do you sell evaluation as a standalone audit?
The sprint includes the evaluation plan and a first set. A long-term harness is part of the production build, not a separate consulting brand.
Can you evaluate a system someone else built?
Yes, if we can run it against cases and see traces. A black box with no logs gets a narrower, harsher memo.
What is AI observability in this context?
Logs and traces for prompts, retrieval, tools, errors, latency, and cost, with access control on the logs themselves.
How many test cases are enough?
Enough to cover the failures that would hurt, including refusals. A hundred easy questions and zero permission cases is not a set.
Related
The rest of the implementation map
AI implementation
Part of the same prototype-to-production path.
Prototype to production
Part of the same prototype-to-production path.
Enterprise deployment
Part of the same prototype-to-production path.
Production readiness
Part of the same prototype-to-production path.
AI agents
Part of the same prototype-to-production path.
RAG development
Part of the same prototype-to-production path.
Enterprise generative AI
Part of the same prototype-to-production path.
Security and governance
Part of the same prototype-to-production path.
Healthcare AI
Part of the same prototype-to-production path.
AI Production Sprint
Part of the same prototype-to-production path.
Bring the prototype, not a wishlist.
The AI Production Sprint is two weeks and fixed fee. You leave with a go/no-go, architecture and security notes, and a next-step quote you can decline.