Generative Ai
Traces to Insights: Evaluating LLM Apps
Introduction
Tracing helps answer an important question: what happened? But knowing what happened isn’t the same as knowing whether it was any good. That’s where evaluation comes in. And if you already have traces running in production, you’re closer to a solid evaluation system than you might think.
In this post, we’ll walk through the core evaluation loop, how to build an evaluation dataset from production traces, and how to use evaluation signals to debug and improve your application.
Introduction
In the previous post , we explored the foundations of LLM observability and tracing. We discussed how traces give you visibility into your LLM application by capturing the full sequence of events across every request, from the initial prompt all the way through retrieval steps, tool calls, and model responses.
Evaluation is the process of systematically measuring how well your AI system performs across the full pipeline, from the intermediate steps like retrieval and tool use through to the final response. Rather than relying on intuition, anecdotal feedback, or reactive debugging, evaluation provides a structured way to determine whether your application is performing as intended. It transforms quality from a subjective judgment into something that can be measured, tracked, and improved over time.
Evaluating LLM applications comes down to a set of questions that traces help you answer: whether the model’s response was accurate, whether it actually solved the user’s problem, whether the retrieved context was relevant to the query, and whether the latest prompt change moved output quality in the right direction or quietly made things worse.
The cost of skipping evaluation
Before diving into how evaluation works, it’s worth being honest about what happens without it.
Without a systematic way to measure quality, improving an LLM application becomes largely guesswork. You fix one issue and introduce another. Your prompts grow longer and more complicated as you attempt to patch edge cases one by one. Performance tuning is speculative at best, there is no way to be sure if a change made things better or just different. And at scale, cost control becomes nearly impossible when you have no visibility into where your tokens are actually going.
A good evaluation system changes this. It makes debugging targeted, optimization data-driven, and improvement continuous.
What evaluation actually means for LLM apps
Unlike most software testing, where the expected output is deterministic, evaluating LLM applications means contending with open-ended, context-sensitive outputs that cannot be reduced to a simple pass/fail.
At a high level, LLM evaluation usually focuses on three dimensions: quality, behavior, and cost efficiency.
Quality
Quality measures whether the application completes the task successfully. Is the answer accurate? Is it grounded in the right context? Does it handle uncertainty appropriately? Does it fail gracefully when it cannot answer? Quality evaluation helps define what an acceptable output looks like and what kinds of errors matter most for your application.
Behavior
Behavior measures whether the system acts the way you intended across different scenarios. This includes consistency, instruction following, tone, refusal behavior, tool usage, and edge case handling. A model may produce a high-quality answer in one case but behave unpredictably when the input changes slightly. Behavior-focused evaluation helps identify those inconsistencies before they become production issues.
Cost efficiency
Cost efficiency measures how much work the system does to produce a useful result. In LLM applications, cost is not limited to the final model call. Tokens may be spent during retrieval, tool selection, planning, summarization, intermediate reasoning steps, and retries. When evaluation is connected to tracing, you can inspect token usage trace by trace and span by span, making it easier to see where cost is accumulating and whether that cost is justified by better outcomes.
The organizing philosophy that ties these together is sometimes referred to as Eval Driven Development (EDD). Before making changes to prompts, models, retrieval pipelines, or agent workflows, you define what success looks like and create datasets that represent the tasks your system needs to perform.
The most useful evaluation datasets are usually task-specific. Off-the-shelf benchmarks can be helpful for understanding general model capabilities, but they rarely capture the details of your application’s domain, users, constraints, or edge cases. A support chatbot, a code review assistant, and a document analysis workflow all need different evaluations because they fail in different ways.
Once those evaluations exist, they become a feedback loop for development. In this sense, evaluation is not a one-time quality gate. It is the foundation for continuously improving an LLM application. Tracing helps you see what happened inside the system. Evaluation helps you decide whether that behavior was correct, useful, and worth the cost.
The core evaluation loop: humans define, models scale
Before you can evaluate at scale, you need a foundation of human judgment. This is where traces become essential in providing domain experts the raw material to work with.
The process works in four stages:
Define. Domain experts manually review a diverse sample of traces and establish what “good” looks like. This means writing explicit success criteria and rubrics that express what the model should and shouldn’t do in specific situations.
Label. Those same experts evaluate a dataset of real traces, marking each one as Pass or Fail accompanied by a short critique explaining their reasoning. A binary system is intentionally simpler than a 1 to 5 scale. LLM judges diverge from human scores , and the disagreement typically worsens as the scoring scale grows more granular, which is why binary classification gives you a cleaner starting signal.
Scale. Once you have a labeled dataset with human critiques, you can use a powerful LLM as a judge to replicate that judgment at scale. The human critiques feed directly into the evaluator prompt as few-shot examples, grounding the model’s assessments in real human reasoning. Use the most capable model you can afford here because this task requires advanced reasoning ability. Before you trust the output, measure precision and recall between the model’s grades and the human labels, and iterate on the evaluator prompt until agreement is comfortably high (in our experience, often in the 80–90% range).
Calibrate. Human evaluation doesn’t stop once the model is running. Periodically, reviewers sample a portion of the model’s grades to check for drift. If the model-based evaluator starts diverging from human judgment, which happens for many reasons, you catch it early and retune.
This loop where humans define the criteria and models run the evals at scale is the engine that makes continuous improvement possible without drowning your team in manual review.
Building your evaluation dataset from traces
Your evaluation system is only as good as the data it runs on. This is one of the clearest arguments for investing in tracing early: production traces become the raw material for your evaluation dataset.
A common mistake when building evaluation systems is waiting until you have a perfect dataset before getting started. In practice, most teams begin with limited data and gradually improve their evaluations as their application matures.
Start synthetic if you have to. If you’re early in development and don’t yet have a body of production traces, bootstrapping with synthetic test cases or open-source data is a reasonable starting point. But treat this as temporary scaffolding, not a foundation.
Migrate to production data as soon as possible. Once your application is serving real users and collecting traces, those traces become one of the most valuable assets for improving quality. Every trace represents a real interaction, complete with user intent, contextual information, model decisions, tool calls, and outcomes. Rather than guessing how users might interact with your system, production traces show you exactly how they do.
Label strategically. Many teams immediately gravitate toward numerical scoring systems such as rating outputs on a scale from one to ten. While these scores appear more precise, they often introduce significant disagreement between reviewers. Even expert evaluators frequently differ by several points when assigning scores to the same response, making it difficult to establish reliable thresholds and compare results over time.
For this reason, it is often better to begin with binary classification tasks. Instead of asking whether a response deserves a score of seven or eight, ask simpler questions: was the response relevant to the user’s request? Did the answer remain consistent with the provided context? Was the retrieved information used correctly? Did the system follow the expected behavior?
Consistency and relevance are particularly effective starting points because they capture two of the most common failure modes in LLM applications while remaining straightforward for both humans and automated evaluators to assess.
When you’re starting out, review as much trace data as you can. Over time, as your dataset grows and your model stabilizes, you can sample more selectively. Three sampling strategies are worth knowing. Failure-driven sampling prioritizes traces the model flagged as low-confidence or that received poor scores, which is where your gaps are. Precision-focused sampling selects instances the model predicted as positive with high confidence, to catch false positives slipping through. Calibration sampling targets borderline cases where the model was uncertain, since those examples sharpen the evaluator’s judgment on the edges.
Evaluation signals: from automated checks to human review
Not all evaluation signals are equal, and not all of them require the same level of effort. A practical evaluation system layers them.
Automated and heuristic checks
Automated and heuristic checks are your first line of defense. These evaluations are inexpensive, highly scalable, and deterministic. They work particularly well when the success criteria can be expressed as objective rules.
Examples include:
- Output format validation
- Structured JSON schema checks
- Length requirements
- Toxicity detection
- PII detection
- Keyword matching
- Regular expression validation
- Safety and compliance rules
For example, if an application is expected to return valid JSON, a simple parser can immediately determine whether the output passes or fails. If a support chatbot must include a ticket reference number, a regular expression can verify its presence.
These checks are often insufficient for measuring overall response quality, but they are valuable guardrails that catch obvious failures before more sophisticated evaluation methods are needed.
LLM-as-judge
As applications become more complex, many evaluation questions require subjective judgment: is the answer relevant? Did the model correctly use the retrieved context? Was the explanation helpful? These questions are difficult to encode as deterministic rules, which is why LLM-as-judge systems have become increasingly popular.
In this approach, another language model evaluates the output and determines whether it meets specific criteria. Because LLM judges can reason about language and context, they can often approximate the decisions a human reviewer would make while operating at a much larger scale.
This makes them particularly useful for relevance evaluation, faithfulness and grounding checks, instruction-following verification, consistency evaluation, and comparative testing between model versions.
However, LLM judges are not perfect.
They can exhibit biases, overfit to particular response styles, or disagree with human reviewers. Different judge models may also produce different outcomes for the same example.
For this reason, LLM judges should be calibrated against human-labeled datasets whenever possible. Human reviewers establish the ground truth, and the judge is optimized to approximate those decisions at scale. As more human annotations become available, the judge can be continuously validated and improved.
The goal is not to replace humans entirely, but to extend human judgment to a volume that would otherwise be impossible to review manually.
Human review
Human review remains irreplaceable for certain tasks. Experienced domain experts can often identify subtle issues that automated systems and LLM judges miss entirely. They understand business context, user expectations, regulatory requirements, and nuanced quality concerns that are difficult to formalize.
Human review is particularly valuable when:
- Defining evaluation criteria for a new application
- Investigating novel failure modes
- Reviewing high-impact decisions
- Evaluating subjective qualities such as usefulness or clarity
- Measuring alignment with business requirements
- Creating high-quality training and evaluation datasets
In many organizations, subject matter experts become a critical part of the evaluation workflow. Their annotations help establish ground truth, identify emerging problems, and provide the feedback needed to continuously improve prompts, retrieval systems, and agent behaviors.
This is often where traces become especially valuable. Rather than reviewing isolated outputs, experts can inspect the entire execution path, including retrieved documents, tool invocations, intermediate steps, and model decisions.
Sometimes the most valuable evaluation question is simply: “Would I be comfortable shipping this response to a customer?”
No automated metric can fully replace that judgment.
Beyond quality scores, your traces expose a rich set of signals worth monitoring. A well-instrumented trace surfaces application latency at the span level, so slow LLM invocations are identified precisely rather than buried in an end-to-end average. Token consumption is tracked per call, making it straightforward to spot inefficient prompts before they become a cost problem. In RAG pipelines, retrieved documents are recorded along with their relevance scores and rankings, giving you visibility into retrieval quality independently from generation quality. The prompt templates used at each step are captured for prompt-level debugging, alongside the LLM parameters (temperature, top-p, and other settings). Tool calls are recorded with their full function signatures, input parameters, and outcomes, so agentic behavior is inspectable rather than opaque. Finally, runtime exceptions such as rate limit errors, timeouts, and other failures are captured at the span level.
Using traces to evaluate, debug, and improve
This is where the value of tracing becomes most tangible. Once your evaluation system is running and scores are flowing back into your trace data, you can use that combined view to systematically improve your application across three dimensions.
Offline evaluation is your safety net before deployment. Before shipping a new prompt, a model change, or a new retrieval strategy, you run your evaluation dataset against the new version and compare scores to the baseline. Only ship if things improved or at least didn’t regress.
Online evaluation runs continuously against live production traces. Automated evaluators score incoming requests in real time, giving you a live quality signal that catches degradation as it happens rather than after users have already noticed. When scores drop, you have the traces to investigate immediately.
When something goes wrong, traces give you a precise map of what happened. Instead of guessing which part of your pipeline failed, you can follow the span tree to the exact step where things broke down:
When a workflow produces bad output, the trace shows exactly where things went sideways. Prompt failures are visible in the raw model response. Tool errors are captured as span events. Retrieval issues show up in the exact documents retrieved and their relevance scores and orchestration bugs such as loops, unexpected branching, and bottlenecks in multi-step agents become visible as anomalous span patterns.
Root cause analysis that once required reproducing issues locally now starts with filtering traces by low evaluation scores and reading the span tree.
With evaluation scores linked to individual traces, optimization becomes targeted rather than speculative. Span-level data makes several improvement workflows more concrete and systematic. When traces are scored and a prompt is underperforming, you can filter down to the worst performers, read through the critiques, identify the recurring pattern, update the prompt, and re-run evals.
Latency tuning benefits from the same granularity. Rather than guessing from an end-to-end average, you can see exactly which component is slow and address it directly, whether the culprit turns out to be a retrieval step, a particular model, or an inefficient tool call. Token usage data works similarly for cost. Seeing consumption broken down at the span level reveals which parts of your application are more expensive. Linking that spend to specific workflows or user segments takes it a step further, replacing a high-level aggregate bill with a per-feature breakdown that engineering and product teams can actually reason about together.
Conclusion
Tracing gives you visibility into what happened while evaluation gives you the understanding to act on it. Together, they form the feedback loop that turns an LLM application from something you ship and hope for the best into something you can measure, debug, and systematically improve.
The path forward is straightforward in principle, even if the details take work: instrument your application, collect traces, build a labeled evaluation dataset from real production data, establish a human-in-the-loop grading process, scale it with model-based evaluation, and run it continuously. Each iteration makes your evals sharper, your prompts better, and your application more reliable.
In our experience, teams whose LLM applications hold up over time tend not to be distinguished by model quality . What sets them apart is having feedback loops that tell them when things are working and when they’re not.
Working on an LLM application and not sure where to start with evaluation? Let’s talk!