Quantifying LLM Output Quality with Pydantic Evals and Integrating It into CI Quality Gates

Introduction: The Non-Determinism Problem with LLMs

Large language model (LLM) outputs are inherently stochastic — the same input can produce noticeably different results across different calls. This randomness stems from the temperature parameter and sampling strategies in the LLM generation process. During inference, the model doesn't simply output the highest-probability token; instead, it samples from a probability distribution. The temperature parameter controls how "flat" that distribution is: higher temperature means more diverse output, lower temperature means more deterministic output. Even with temperature set to 0, some inference frameworks still exhibit floating-point non-determinism at the hardware level, leading to output variance across different hardware configurations and batch sizes. This randomness isn't a flaw — it's the source of an LLM's creativity and generalization ability — but it's precisely what causes traditional deterministic testing frameworks to break down.

This characteristic creates a fundamental tension with traditional software engineering practices. Conventional code tests yield a clear pass or fail; LLM outputs simply can't be measured the same way.

This is the core challenge many teams face when integrating LLMs into production systems: how do you establish reliable quality gates for LLM performance within a continuous integration (CI) pipeline? One developer shared his practical approach using Pydantic AI and Amazon Bedrock, with the central idea being to transform vague "output quality" into a concrete number that can be used to block merges.

Why Single-Run Test Results Are Unreliable

"False Failures" and "False Passes" from Randomness

If you run a test only once to determine whether an LLM meets the bar, the result is typically flaky. One call might pass by chance due to randomness, while the next fails — and vice versa. This instability causes CI pipelines to constantly misfire, and teams end up either ignoring alerts or abandoning automated quality checks altogether — both outcomes are highly risky.

A far more reliable approach is to run tests repeatedly to calculate a pass rate rather than relying on a single result. By running the same test case multiple times and recording the success ratio, you get a statistically robust quality metric. For example, a test case that passes 8 out of 10 runs yields an 80% pass rate — far more reflective of the model's true behavior than a binary pass/fail judgment.

Structured Output: A Foundation for Meaningful Evaluation

Pydantic AI's Structured Output Capabilities

A prerequisite for evaluating LLM output is that the output itself must be parseable and verifiable. Pydantic AI has a natural advantage here — it's an AI application development framework built on the Pydantic data validation library. Its structured output capabilities rely on two primary mechanisms: constraining model output via JSON Schema (Function Calling / Tool Use), and using model APIs that natively support structured output (such as OpenAI's Structured Outputs and Anthropic's Tool Use). Developers define their expected data structures as Python classes, the framework automatically converts them into schema descriptions the model can understand, and upon receiving a response, Pydantic performs strict type validation and field checking. If the model's output doesn't conform to the schema, the framework can automatically trigger a retry or raise an error.

Once output is constrained to a structured format, the evaluation task shifts from "interpreting a block of free text" to "verifying whether fields match expectations" — dramatically reducing complexity and making it far more suitable for automation. This mechanism is the critical bridge connecting LLMs to an engineering-grade evaluation system.

Combined with the managed model capabilities of Amazon Bedrock, developers can run this evaluation system stably on enterprise-grade infrastructure. Bedrock is AWS's fully managed foundation model service platform, allowing enterprises to access models from providers including Anthropic (Claude series), Meta (Llama series), and Mistral through a unified API — without managing model deployment or infrastructure themselves. Its enterprise-level security and compliance guarantees (data is not used for model training, VPC private deployment supported) and deep integration with AWS IAM permissions make it especially well-suited for handling the high volume of repeated evaluation calls in a CI pipeline.

Building Quantifiable Evaluation Dimensions

Pass Rate, Latency Budget, and LLM Judge

A complete LLM evaluation framework should cover three key dimensions:

1. Pass Rate Obtain a statistically meaningful pass rate through repeated runs, replacing fragile single-run results. This is the cornerstone of the entire approach.

2. Latency Budget Response speed is also a hard requirement in production environments. Set an explicit latency budget for your evaluations to ensure that the model not only answers correctly, but answers fast enough — calls that exceed the budget may be deemed unacceptable even if the result is correct.

3. LLM Judge For output quality that's difficult to assess with deterministic rules, you can introduce an "LLM as judge" mechanism — using one model to evaluate the output of another. LLM-as-a-Judge is an evaluation paradigm systematically formalized by Zheng et al. in 2023. The core idea is to use a more capable model (such as GPT-4 or Claude 3 Opus) to score or rank the outputs of the model under test. This approach can evaluate dimensions like semantic relevance and logical coherence that are difficult to express as rules, at a cost far lower than large-scale human annotation. But this judge shouldn't be trusted unconditionally.

Calibrating the Judge: Trust but Verify

One step that's easy to overlook — but critically important — is this: before relying on an LLM judge's scores, you must calibrate it against human annotations.

LLM judges are known to exhibit several systematic biases: position bias (a tendency to favor the first option presented), verbosity bias (a preference for longer responses), and self-preference bias (models tend to rate their own outputs more favorably). These biases can systematically distort evaluation results — blindly trusting a judge's scores simply swaps one source of uncertainty for another. The correct approach is to compare the judge's scores against human-labeled ground truth, confirm that the agreement level is acceptable (research suggests a well-calibrated LLM judge can achieve over 80% agreement with human annotations), and only then incorporate the judge's scores into your decision-making process. This step is the foundation of the entire automated evaluation system's credibility.

Implications for Engineering Practice

Bringing AI Quality into Engineering Discipline

The core value of this methodology is that it transforms what was previously a subjective, fuzzy question — "is the LLM performing well?" — into a measurable, automatable, gate-able engineering metric. A quality gate is a checkpoint in a CI/CD pipeline that automatically verifies a predefined set of quality standards before code is merged or deployed. Incorporating LLM pass rates into quality gates requires balancing evaluation coverage against CI execution efficiency, setting reasonable thresholds based on historical baseline data, and continuously tracking pass rate trends to detect model regression early. The maturity of this mechanism is an important indicator of a team's AI engineering sophistication.

Underpinning all of this is a mature engineering philosophy: don't try to eliminate LLM randomness — manage it at the statistical level.

For teams actively integrating LLMs into production systems, the following points are especially worth noting:

• Never make quality judgments based on a single call result — run tests repeatedly and calculate a pass rate
• Quality evaluation must be multi-dimensional; functional correctness and latency performance are equally important
• Introducing an LLM judge can extend evaluation coverage, but it must first be calibrated against human annotations to eliminate systematic errors such as position bias and verbosity bias
• Structured output is a prerequisite for automated evaluation; tools like Pydantic AI can significantly lower the barrier to implementation

Summary

As LLM applications move from prototype to production, establishing reliable quality assurance mechanisms within CI/CD workflows is becoming a central challenge in AI engineering practice. The approach described here — built on Pydantic AI and Amazon Bedrock — offers a pragmatic and replicable reference model. Rather than sidestepping the fundamental challenge of LLM non-determinism, it uses statistical methods, latency constraints, and a calibrated judge mechanism to converge uncertainty into a concrete number capable of blocking a merge. This is precisely the kind of step needed to genuinely integrate AI capabilities into software engineering discipline.