🧪 What Are Evals?
As language models and agentic systems become more powerful, evaluating their behavior becomes increasingly critical.
Unlike traditional software, LLM-based systems:
- Don’t have deterministic outputs
- Can solve tasks in many valid ways
- May rely on external tools or memory
- Are often judged by human-like reasoning, not simple correctness
That’s why we need structured evaluations, or evals.
🎯 What Are Evals?
An eval is a test case that measures how well a system performs a task. It usually includes:
- An input — like a prompt, question, or plan.
- An expected behavior — what a good answer or process looks like.
- One or more metrics — quantifying quality, correctness, efficiency, or alignment.
Evals let us ask:
- Did the model produce a useful output?
- Did it follow the right steps?
- Did it use the right tools?
- Did it complete in a reasonable time?
- Did it behave consistently across changes?
🔍 Why Are Evals Important?
LLMs and agents don't fail like traditional code. Instead, they:
- Hallucinate answers
- Misuse tools
- Produce results that seem plausible but are subtly wrong
- Behave inconsistently with small input shifts
These aren't bugs — they’re behavioral drift. And without evals, they’re nearly impossible to detect at scale.
Evals provide:
- ✅ Confidence before deployment
- 📉 Regression detection during iteration
- 📊 Benchmarks for tracking improvement
- 🧪 Insight into how your system behaves under stress or edge cases
🧩 What Makes Evaluation Hard?
Evaluating agents is different from testing conventional software.
| Challenge | Why It Matters |
|---|---|
| 🧠 Multiple valid outputs | There’s rarely one “correct” answer |
| 🔁 Stateful / multi-step | Behavior depends on memory, history, and tool use |
| 🧰 Tool use & side effects | Execution isn’t just text — it may call APIs/tools |
| 🤖 Subjective quality | Human judgment often needed to assess usefulness |
| 🔁 Evolving models | Model versions can subtly shift behavior |
You can’t write a single assert output == expected test and be done. You need a broader framework to evaluate performance meaningfully.
📐 Types of Metrics
Good evals result in metrics — numerical scores that help track progress or catch regressions.
Examples include:
- Correctness — Did the system return the right answer?
- Completeness — Were all relevant points included?
- Clarity — Was the output easy to follow?
- Efficiency — Were unnecessary steps avoided?
- Latency — How long did it take?
- Tool usage — Did it use the expected tools?
These can be generated by:
- ✍️ Rule-based checks (e.g., string match)
- 💬 LLM judges ("Does this answer the question well?")
- 🧪 Custom logic specific to your domain
🧪 Evals vs. Unit Tests
| Unit Tests | Evals |
|---|---|
| Binary pass/fail | Often graded on a scale (0.0 to 1.0) |
| Deterministic | Tolerate variability |
| Focused on internal logic | Focused on observable behavior |
| Fail fast | Interpret trends over many cases |
| Used in all software | Essential for ML/LLM/agent-based systems |
Both are valuable — but for anything involving LLMs, evals are essential.
🎓 Who Should Use Evals?
Anyone building systems that rely on:
- Language models (chatbots, assistants, generators)
- Agent frameworks (planners + tools)
- Automated workflows
- Retrieval-augmented generation (RAG)
- Chain-of-thought reasoning
Evals let you move from guessing to measuring — replacing intuition with evidence.