See which one wins.

You're choosing between models for a RAG-powered support bot.

• Manual spot-testing on a few tickets feels productive but gives you basically no reliable signal
• The real issue is that models diverge on hard tickets. Simple lookups score fine across the board, complex reasoning is where you’ll actually see the gaps
• Run all your candidates against your real ticket dataset, slice by category and difficulty, and see the tradeoffs laid out in one place. Then you can make the call with actual data.

You need an LLM to summarize contracts or legal documents and you need to trust what it produces.

• The tricky part is that bad output doesn’t look bad. Models miss liability clauses or drop carve-outs while producing perfectly clean-looking text
• Faithfulness failures are invisible without measurement, and that’s exactly where teams get burned
• Score your models on faithfulness and completeness against real document samples. You’ll quickly see which ones you’d actually trust before they go anywhere near a client.

You tweaked your system prompt and it feels better. Now you need to know if it actually is.

• The frustrating thing is you probably improved some cases and regressed others without realizing it
• Without tracking, prompt changes are basically vibes. You’ll find out what broke in production, not before
• Compare every version across your full test set and see exactly which cases improved and which regressed. Ship with a real diff, not a gut feel.

You're evaluating LLMs for clinical note summarization or documentation workflows.

• Standard benchmarks won’t tell you much here. They don’t reflect your actual documents or the edge cases that matter in your context
• A model that scores well on general evals can still drop critical details on complex cases. In clinical workflows completeness is not a nice to have
• Evaluate against your own document types, score on the dimensions that actually matter, and have something concrete to show compliance when they ask.

Your RAG pipeline is underperforming and you're trying to figure out where to focus.

• The annoying part is you can’t tell whether retrieval or the LLM is the weak link just by looking at outputs
• A lot of models will silently ignore retrieved context and fall back on what they already know. So you get confident-sounding wrong answers even when the right chunks are sitting right there in the prompt
• Hold retrieval constant, swap the LLMs, and see clearly which layer is actually failing. Saves you from optimizing the wrong thing.