Why LLM evaluation is the most underestimated part of any AI project
A model that looks good in a demo isn't a model that works in production. Once you deploy LLMs seriously, you need an answer to: how do we know it's good? And more importantly: how do we know it's better? This is where most AI projects fail. Not at the model. Not at the prompt. They fail because nobody systematically measures whether the system does what it should. LLM evaluation is the discipline that answers this - and it's just as important as the system itself.
What good LLM evaluation includes
Robust LLM evaluation has at least three layers. Offline evaluation: test sets with known correct answers, automatically scoreable (accuracy, F1, BLEU, ROUGE - depending on the task). Plus semantic evaluation with LLM-as-a-judge where deterministic metrics fall short. Online evaluation: A/B tests in production that measure real user signals - conversion, satisfaction, efficiency. Monitoring & drift detection: continuous observation of unexpected changes (model updates, data drift, prompt regressions). Only together do they give you a reliable picture.
Evaluation for AI agents - where it gets really hard
For simple LLM tasks (classification, summarization), standard metrics are enough. For agents that plan multiple steps, call tools, and self-correct, they're not. You have to measure: does the agent achieve its goal at all? Does it follow policies (e.g. "always confirm before deleting")? Does it work efficiently (not 50 tool calls when 5 would do)? Does it behave consistently across comparable requests? Hard-won lessons from real production systems flow directly into our consulting.
Frameworks and tools we typically use
Depending on the use case: DeepEval for structured LLM tests with semantic metrics. Langfuse or Phoenix (Arize) for observability and trace analysis in production. τ²-bench for agent simulation and policy-conformant evaluation. Promptfoo for lightweight A/B testing of prompts. A custom test set built from your real data - without it, everything else is worthless. We help you pick the right framework for your situation without locking you into specific vendors.
How we help with LLM evaluation
Three typical engagements. Evaluation audit (1 to 2 weeks): we review your existing AI system and tell you what your current eval strategy can and can't catch. Output: concrete recommendations with effort estimates. Eval framework build (3 to 6 weeks): we build an automated evaluation framework for your specific use case, including test set construction, metric definition, and CI integration. Eval as part of an implementation: every AI implementation we deliver includes an eval framework from day one - not as an afterthought.
What makes this LLM evaluation consulting different
You get a team that has spent years on the production side of LLM evaluation. We have built production AI systems at Amazon Alexa, Bosch, and Meta - and learned the hard way every time what happens when eval comes too late. That practitioner perspective is rare in the DACH market.