What does an LLM or RAG evaluation engagement produce?
Typical outputs include a representative task set, retrieval checks, answer-quality rubric, regression tests, model or vendor scorecards, readable reporting, and engineering handoff notes.
LLM / RAG / evaluation
AI systems are only useful when you can measure them.
We design retrieval systems, evaluation harnesses, regression tests, and scorecards that make LLM behavior visible to engineering and leadership at the same time.
Evaluation before scale
LLM systems often look promising in demos and become fragile in production. The difference is usually evaluation: representative tasks, clear scoring, regression coverage, and reporting that shows whether a change made the system better or worse.
We help teams create retrieval and evaluation layers around real workflows, business constraints, and edge cases. The goal is a system your team can inspect, improve, and defend.
Useful for RAG, vendors, and internal AI tools
This work fits teams building RAG systems, comparing LLM vendors, improving answer quality, evaluating agent workflows, or preparing an AI system for broader internal or customer-facing use.
What good measurement includes
Useful evals are tied to the tasks the system actually performs. For a RAG workflow, that means source recall, citation quality, permission checks, answer usefulness, hallucination risk, and what happens when the right answer is not in the corpus. For a model or vendor comparison, it means quality, latency, cost, data handling, reliability, and operational fit.
The reporting should be readable by leadership and useful to engineering: a clear view of what works, what fails, how severe the failures are, and which changes improve the system without making another part worse.
Answers
Questions teams ask before trusting an LLM system.
How do you know a RAG system is good enough?
It retrieves the right sources for representative questions, exposes usable citations, handles edge cases, and meets agreed thresholds for answer quality, risk, and review effort.
Should evaluation happen before or after launch?
Both. Pre-launch evals catch basic quality and safety problems; post-launch regression tests catch drift, model changes, retrieval changes, and workflow regressions.
Can ideius compare LLM vendors or models?
Yes. The comparison should use your real tasks rather than generic benchmarks, weighing quality, cost, latency, reliability, data handling, tooling, and operational fit together. For a published example, see our benchmark of two quantizations of the same model on an agentic tool-calling task.
What makes an eval useful to leadership?
Leadership needs to see where the system works, where it fails, how severe the failures are, what improvement costs, and whether the current version is ready for the intended users.
How does this relate to agents?
Agent workflows need evals for tool use, recovery, routing, approvals, and handoff quality. That is why evaluation often sits underneath AI agents and automation.
Next step
Need evidence before the next rollout or vendor choice?
Bring the system, task set, or evaluation question that needs to become measurable.