A test set from real work
Define expected outcomes with domain owners. Include common cases, edge cases, missing evidence, and examples where the system should decline or escalate.
LLM evaluation and AI quality testing
Evaluate an existing AI assistant, RAG system, or agent before expanding it. Redko helps build representative test sets, failure analysis, release checks, and a plan to improve quality, latency, and operating cost.
What we can build
Define expected outcomes with domain owners. Include common cases, edge cases, missing evidence, and examples where the system should decline or escalate.
Assess answer usefulness, citation accuracy, retrieval, tool use, and successful completion. Inspect failures rather than relying on one aggregate score.
Compare changes to prompts, models, retrieval, and tools against the same baseline. Track cost and latency alongside quality.
How we work
Agree the workflow, impact of errors, available examples, and acceptance criteria. Separate offline evaluation from observed live behavior.
Combine programmatic checks with review where judgment is required. Group errors by cause and prioritize actionable fixes.
Deliver the evaluation setup and findings, then agree which fixes or ongoing monitoring belong in the next scope.
No. This is engineering evaluation against an agreed scope. Formal certification or specialist legal and compliance review is a separate process.
It can identify opportunities such as smaller models, shorter contexts, caching, and fewer tool calls. Savings must be measured against quality and workload.
Yes. Access to the implementation, sample data, and expected behavior determines what we can assess.
Let’s build something
An idea, an existing product, or a workflow that takes too much time. Tell us where you are, and we’ll talk through a useful next step.
Prefer email? info@theredko.com
No account required. A conversation before a commitment.