Why Aegis

There is no shortage of frameworks, platforms and tools when it comes to evaluating LLMs. So why choose Aegis? If you are asking yourself this question, you are in the right place. In this article, we will look at eight things to consider when choosing an evaluation platform.

1. Coverage of the metrics you actually need

Let's start with an analogy. Think of an annual motor vehicle inspection. The examiner does not give you one mystery score called “overall car quality.” They work through distinct items (brakes, lights, tyres, suspension, emissions, and so on), and each maps to safety or emissions rules that actually matter on the road. You can pass headlights and still fail on brake wear, so collapsing everything into “the car drives” would hide the failure modes you bought the inspection to find.

LLM evaluation is similar. List the risks that matter for your use case (RAG quality, safety, role violation, structured output, bias, leakage, whatever applies) and check those scores explicitly, not only one vague headline number. When you compare vendors, you have to look deeper into the details and ask yourself if the metric you are using is specifc enough to catch the failures you care about in your product and, if necessary, to combine multiple metrics to create a "health checklist", similar to a car inspection.

One research survey also summarises LLM evaluation across knowledge, alignment, safety, and similar axes, and stresses that one headline score rarely tells the whole story (Guo et al., comprehensive survey).

Aegis focuses on specialised metrics grounded in context and use cases, so you see which dimension failed, not only that “something went down.” That makes root-cause work easier than leaning on a single one-size-fits-all score. You can check all the metrics on the documentation page, where they are categorized into different areas of evaluation: https://docs.aegisevals.ai/docs/metrics/metrics-introduction.

2. Deterministic scoring vs. LLM-as-a-judge vs. hybrid approaches

Most evaluation stacks use one or more of three patterns. None is “best” on its own; they differ in what you can prove, what you can express, and what each run costs.

Deterministic scoring (rules, regex, schema validation, exact match, scripted checks):

• Repeatable: the same input and rubric give the same result, which helps regression tests and audits.
• Fast and cheap: you are not paying for a second model call on every row, so you can run checks on large batches.
• Clear pass/fail: failures map to concrete rules (“invalid JSON,” “missing field,” “forbidden phrase”), which is easier to debug than a vague low score.

LLM-as-a-judge (a model scores or ranks another model’s output against a rubric):

• Handles nuance: useful when quality is subjective or open-ended (tone, helpfulness, partial correctness) and hard to encode in code alone.
• Scales without full human review: you can score many samples automatically while still capturing dimensions a single regex cannot see.
• Flexible rubrics: you can refine prompts and criteria as the product evolves without rewriting a large rule engine each time.

Hybrid approaches (deterministic gates plus LLM judges on the same pipeline):

• Right tool per check: cheap rules catch structural and policy breaks; judges focus effort where judgment actually matters.
• Better cost control: you avoid running an expensive judge on rows that already failed an obvious deterministic test.
• Stronger coverage: structure and safety can be enforced rigidly while softer quality dimensions still get a scored, explained assessment.

What matters in practice is how much variance you can tolerate, whether you need evidence a regulator or security team can follow, and whether your failures are mostly “wrong shape” or “wrong judgment.” Many production setups use all three layers.

3. Single-step vs. multi-step evaluation logic

Some checks are a single pass (for example “is this valid JSON?” or an exact string match). Others are built as a short pipeline inside one metric: prepare the inputs, run one or more sub-checks, then combine the outcome into the 0–100 score you see in the UI or API. In Aegis, several metrics work this way rather than asking one model for one number and stopping.

Typical patterns include:

• RAG-style metrics (such as Context Faithfulness, Context Recall, or Context Relevancy) that first relate the answer to retrieved material, then judge alignment or coverage at a finer grain (for example span- or chunk-level checks) before the final score is produced.
• Hybrid metrics that combine deterministic steps (parsing, schema checks, filtering) with an LLM judge on a rubric, so the published score reflects more than one decision.
• Safety and policy metrics (such as Role Violation or PII-PHI Leakage) that may run staged checks on different parts of the conversation or output before aggregating into one result.

That internal depth is why two platforms can both offer “faithfulness” while measuring different things. When you compare vendors, ask not only whether they support a metric name, but how many logical steps sit behind it and whether you can see enough detail in explanations and metadata to understand which step failed.

The metrics introduction in the Aegis documentation describes each metric’s intent; for multi-step designs, the per-score explanation is often where you see which part of that pipeline drove the number down.

4. Result scale and score interpretation

A score only helps if everyone knows what the number represents and what to do next. Two things usually make that workable: a consistent scale and a short explanation of why the score landed where it did.

Scaling (what the number means). It helps to know whether you are getting categories, a small Likert band, a 0–100 style range, or something else, and what a point or threshold is supposed to mean for your release process. Agreeing on that with engineering and product early saves weeks of “but your 72 is not my 72” later.

• Example: On a 0–100 scale, Context Faithfulness at 72 on a RAG assistant might mean the answer is mostly tied to retrieved chunks, with small unsupported additions. After a prompt change, the same metric falls to 45 while Answer Relevancy stays near 80. You can see grounding broke even though the reply still looks on-topic; pass/fail alone on both runs would only say “fail” twice, with no sense of how far you moved.
• Example: Aegis scores each metric separately (e.g. Factfulness, Context Recall, Role Violation, PII-PHI Leakage). You might gate release on Context Faithfulness ≥ 70 and Role Violation ≥ 75, but treat Summarization below 60 as a warning, not a hard block. Same 0–100 range, different thresholds per risk, tracked over runs in the platform.

On Aegis, metrics are scored on a 0–100 scale rather than a single pass/fail bit, so you can compare runs, spot gradual drift, and tune gates without collapsing everything into one red/green light.

Explanation (why you got that number). The score answers “how much”; the explanation answers “because of what.” Without that second part, a low score sends you back to manual reading of hundreds of rows.

• Example: For Role Violation, a score of 38 on its own only signals that a problem exists. The explanation might spell out how the answer breaks the assigned role, for instance that the assistant answered as a general legal adviser when the system prompt limits it to summarising internal policy documents, or that it promised actions only a human agent may take. You learn which part of the role definition was crossed, not just that the run failed a safety-style label.
• Example: Score 85 on answer relevancy might look fine until the rationale says the reply ignored the user’s length limit and answered a different sub-question. The number suggested “good enough”; the reasoning shows why product would still reject it.

Together, scale and explanation support different jobs: the scale helps you spot that a problem exists, whereas the explanation points to the root cause (“change the rubric,” “fix retrieval,” “tighten the role prompt”). That pairing is what makes results easier to interpret in standups and in postmortems, not only on a dashboard average.

5. Level of explainability

Explainability should be a key feature of any evaluation platform to really help you understand the results. In the case of Aegis, whether you are using the web version or the API, when you are looking at a single metric score or the entire evaluation run, every metric score comes with its own explanation: a short account of why that metric landed where it did for that row. Context Faithfulness, Role Violation, PII-PHI Leakage, and the rest can fail for different reasons; a single global note does not replace per-metric reasoning. When you open a low Role Violation score, the text should say how the answer stepped outside the role, not only that the number is low.

That per-score explanation is the main way teams move from “something failed” to “this is what we change.” It matches what we described in section 4: the number is the signal, the prose is the diagnosis.

Aegis goes one step further by attaching metadata to each metric result. Alongside the score and its explanation, you get structured context about that evaluation. You can filter, export, or join that metadata in your own analysis to compare failures across prompts, models, or dataset slices. The explanation tells you the story in plain language; the metadata helps you prove, group, and quantify patterns, for example “most Role Violation drops this week share the same system prompt version” or “Context Faithfulness failures cluster on long retrieved chunks.”

Used together, per-metric explanations and metadata make evaluation feel closer to observability: scores stay comparable over time, and the path from a number to a fix stays visible to engineers, reviewers, and product owners who were not in the room when the run happened.

6. Evaluation latency

If each run is slow or expensive, people run fewer of them. That affects how often teams actually measure and how pleasant the tool is in day-to-day work. This is a practical constraint as much as a technical one.

In Aegis, both the web app and the API let you see when a run took place and how long each row in a dataset took to evaluate. You can track per-row duration across a batch, spot outliers that dominate wall-clock time, and decide whether the bottleneck is a specific metric, a long input, or the size of the set, without timing things by hand on your machine.

7. Web platform and API

Aegis is available in two ways: a web platform for people who want to work in the product UI, and an API for teams that want to wire evaluation into scripts, pipelines, or internal tools.

Web platform

• Use Aegis as a standalone user or as a member of an organization, depending on how your account is set up.
• Upload datasets and manage the examples you care about in one place.
• Run evaluation on a whole dataset when you want batch coverage, or on a single example when you are iterating on a prompt or debugging one bad row.
• Review results in dashboards that lay out scores, passes, and failures so it is straightforward to see what changed between runs and which metrics drove the outcome.
• Drill into per-metric scores, explanations, and metadata from the same UI (as in sections 4 and 5).

API

• Trigger the same metrics you use in the web app from your own code, so CI jobs, internal services, and notebooks stay aligned with what reviewers see in the UI.
• Run evaluations programmatically against stored datasets or integrated workflows without manual clicks.
• Retrieve scores, explanations, timing, and related fields for downstream storage, alerting, or custom reporting.
• Rely on documented request shapes and auth so engineering can embed evaluation without maintaining a parallel harness.

8. Calibration quality: too strict, too lenient, well balanced

Judges that are always harsh or always generous are both painful: one produces noise in your alerts, the other hides real issues. You do not need perfect calibration on day one, but you do want to notice systematic skew and be able to adjust.

In Aegis, each metric has its own threshold on the 0–100 scale. For a given run, a row fails that metric when the score falls below that threshold (and passes when it meets or exceeds it). That turns a continuous score into a clear gate for release checks, dashboards, and pass-rate summaries, while still letting you inspect the raw number when you need nuance.

Defaults give you a sensible starting point per metric, but the threshold is not fixed: you can adjust it manually to match how strict your product or policy needs to be. Context Faithfulness at 70 might be right for an internal copilot, while a customer-facing assistant might need 80; Role Violation might be held to a higher bar than Summarization. Tuning per metric is part of calibration: if everything fails, the bar may be too high; if risky rows keep slipping through, it may be too low.

Bottom line

Yes, there are many evaluation frameworks and tools. Frameworks are useful for experiments; hosted platforms matter when you want the same checks, history, and review workflow in one place. The question is not whether evaluation is optional, but whether the tool you pick matches how your product actually fails.

When you compare options, the eight areas in this article are a practical scorecard:

1. Metric coverage — named checks for RAG, safety, security, structure, and general quality, not one blended “quality” score.
2. Scoring approach — room for deterministic rules, LLM judges, and hybrids, including multi-step logic inside individual metrics.
3. Evaluation depth — scores that reflect staged pipelines where that matters, not only a final answer grade.
4. Scale and thresholds — a shared 0–100 range, per-metric pass bars you can tune, and explanations that say why a row failed.
5. Explainability — per-metric reasons plus metadata you can filter and export, in the UI and via the API.
6. Latency visibility — timestamps and per-row duration so batch runs stay understandable.
7. Web and API — datasets, single-example runs, dashboards for review, and automation without a separate SDK.
8. Honest comparison — clarity that different tools measure different constructs; paired runs beat marketing claims.

Aegis is built around that checklist. It ships specialised metrics (see the metrics introduction), hybrid and multi-step designs where the construct needs them, 0–100 scores with adjustable thresholds, and explanations on every metric result. You can work as an individual or inside an organisation, upload a dataset or test one example, and use the same evaluation logic from the browser or from code. If you are comparing against other stacks, our write-up Aegis alongside DeepEval, Opik, and DeepTeam walks through pass agreement on shared cases, which is a useful sanity check rather than a verdict on “the best” framework.

Not every team needs every metric on day one. Start with the failures you have already seen in production, turn them into a small set of gates with thresholds you can defend, and expand when new risks appear. For a shorter view of common starting points, see Top 5 essential metrics for evaluating LLMs.

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