Evaluating large language models (LLMs) might seem straightforward at first—test them, collect metrics, and identify areas for improvement. But in practice, the outcomes often fall short of expectations.
This is usually because the evaluation process is misaligned from the start. When we focus too heavily on surface-level metrics, we risk missing what really matters.
In this post, we’ll explore why LLM evaluations often fail, and how to design an evaluation strategy that leads to real insight and meaningful results.
At its core, evaluating an LLM means measuring how well a language-model-powered system performs in practice. The following are commonly used criteria:
Relevance: Does the response match the user’s intent or question?
Factual correctness: Are the statements objectively true?
Accuracy: Does the output align with the expected result?
Usefulness: Is the response actionable or helpful to the user?
Similarity: How close is the response to a known correct answer?
These criteria are essential across a range of use cases—from document summarization and customer service chatbots to code generation and information retrieval tools.
In reality, LLM evaluation is rarely as simple as defining test cases and comparing outputs. That’s because modern systems often rely on a combination of components and variables:
Complex prompt design and instruction tuning
Retrieval-augmented generation (RAG) pipelines
External API or tool integrations
Multi-step or multi-agent workflows
Subjective, inconsistent human labels
Even a well-designed test suite can miss the bigger picture: whether the system is truly helping users accomplish their goals. It might look good on paper, but still deliver a frustrating experience.
If you want to evaluate LLM performance in a meaningful way, keep these five principles in mind:
1. Start with user goals
Before designing metrics, define what success looks like from the user’s perspective.
2. Balance automation with human judgment
Automated tools like GPT-based graders or similarity scores are useful, but human feedback is critical—especially in early stages.
3. Align metrics with business impact
Focus on metrics that reflect real outcomes: task success rates, customer satisfaction (CSAT), or revenue contribution—not just token-level accuracy.
4. Automate to detect regressions
Set up continuous evaluation pipelines to monitor how updates affect performance over time.
5. Metrics are a tool, not the goal
Don’t let metrics become the objective themselves. Use them as a compass to guide decisions, not as vanity indicators.
By keeping these principles in mind, you can move beyond surface-level evaluation and focus on what really matters—delivering consistent value to your users and your business.
Evaluating LLMs effectively isn’t just about what the model says; it’s about what it does for your users.