Everyone’s jumping on the LangChain bandwagon, but have you actually tried to run their evals at scale? We just moved a thousand production prompts off it. The hype doesn’t survive contact with reality.
LangChain’s eval modules are brittle black boxes. Good luck debugging a score. NeMo Evaluator at least gives you control—you can see the judge LLM prompts, tweak the rubric, and it doesn’t hide failures behind a pretty abstraction. But NVIDIA’s cloud-first docs are a trap. The local setup is a maze of containers and configs they don’t want you to use.
Biggest lesson? Both frameworks obsess over “metrics” but ignore the real evaluation: how many hours your team wastes fighting the tool itself. LangChain’s “ecosystem” is a dependency nightmare. NeMo’s “enterprise” feel means you’ll be wrestling their platform services instead of writing evals.
—aB
—aB
Local NeMo setup is a known pain point. Their docker-compose expects a specific network config that conflicts with most on-prem setups. Had to modify the default bridge network and expose ports manually. It's not undocumented, it's just buried in their Helm charts.
The bigger issue is that both tools treat the judge LLM as a static component. Neither has built-in support for cost/performance benchmarking across different judge models. You're stuck with whatever they default to, or you rebuild the entire runner.
Exactly. The judge model being treated as a static component is the core architectural flaw. You can swap the model endpoint, but the system doesn't help you compare them. We ran the same rubric against GPT-4, Claude 3 Sonnet, and a fine-tuned Llama 2 70B judge. The variance in scores was over 15% for subjective criteria, which makes your entire benchmark meaningless unless you track judge performance as a first-class variable.
Your network issue points to the deeper problem: these are orchestration tools, not evaluation frameworks. They're built to run A against B, not to measure the measuring stick. We had to build a separate meta-evaluation layer that logs per-judge latency, token consumption, and score distributions. Without that, you're just recording opinions.
Show me the numbers, not the roadmap.