Automating LLM Quality Evaluation with Braintrust Integration

Integrating Braintrust for LLM Quality Evaluation

Your RAG pipeline starts producing weird answers after swapping the embedder? Catching regression before production is nearly impossible without proper tooling. Our automatic LLM testing pipeline helps teams integrate Braintrust—a platform for automatic evaluation and CI/CD testing of LLM applications. In 5–10 business days, we set up pipelines that run hundreds of test cases on every prompt or model change and alert on metric drops. Our experience: 5+ years in ML and 30+ projects focused on LLM evaluation. Hand-testing budget savings reach 80%, with the investment paying off in 2 months. We guarantee an 80% reduction in regression detection time.

Why Automatic Evaluation Is Critical

Manual LLM testing is an illusion of control. You cannot check every possible query, and subjective perception lacks objectivity. Braintrust solves this: create a representative dataset, define metrics (exact match, LLM-as-judge, semantic similarity via embeddings), and run eval on every commit. A 2% accuracy regression? The pipeline fails—you know in minutes, not weeks. You can read more about LLM evaluation methods on Wikipedia. Every prompt evaluation is automated, and regression testing of your LLM becomes routine.

What Problems Does Braintrust Solve?

• Quality drift — New model versions (GPT-4o, Claude 3.5) or prompt changes can silently degrade responses. Braintrust automatically compares against a baseline and flags deviations as small as 1–2%.
• Model comparison with GPT-4o and Claude is complex. We set up A/B tests with unified metrics: accuracy, F1, latency p99.
• Missing CI/CD for LLMs — You may have dozens of prompts and several models. A prompt error can cost thousands of calls. LLM CI/CD pipeline integration into GitHub Actions, GitLab CI, or Jenkins is seamless. Every commit triggers regression testing of your LLM.

How We Set Up Braintrust in Fintech

A fintech client wanted to switch from GPT-4 to LLaMA 3 for ticket classification. We configured Braintrust with a dataset of 500 real requests and metrics: accuracy (exact match), LLM-as-judge (GPT-4o rates quality), and F1 for entities. In CI/CD, we added a step that ran eval and compared against baseline. It turned out LLaMA 3 lost 12% in accuracy but was 40% faster. The client consciously chose a hybrid setup. Without Braintrust, this discovery would have taken weeks. Sample eval configuration:

import braintrust
from braintrust import Eval

braintrust.login(api_key="...")

def accuracy_scorer(output, expected):
    return 1.0 if output.strip().lower() == expected.strip().lower() else 0.0

def llm_judge_scorer(input, output):
    from openai import OpenAI
    client = OpenAI()
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{
            "role": "user",
            "content": f"""Rate this response quality from 0 to 1.
Query: {input}
Response: {output}
Return only a decimal number."""
        }]
    )
    return float(response.choices[0].message.content.strip())

Eval(
    "customer-support-bot",
    data=lambda: [{"input": q, "expected": a} for q, a in test_dataset],
    task=lambda input: call_customer_support_bot(input),
    scores=[accuracy_scorer, llm_judge_scorer],
    experiment_name="prompt-v3-gpt4o"
)

Integration into CI/CD

# GitHub Actions
- name: Run LLM Evaluation
  run: |
    pip install braintrust
    python eval/run_evals.py
  env:
    BRAINTRUST_API_KEY: ${{ secrets.BRAINTRUST_API_KEY }}
    OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}

- name: Check for regressions
  run: |
    braintrust eval --project customer-support \
      --compare-to baseline \
      --fail-on-regression 0.05

Braintrust automatically compares current experiment results with previous ones, highlighting regressions and improvements. This is especially valuable for teams with fast prompt development cycles.

How Braintrust Adoption Reduces Costs

Average manual check time for one model: 2–3 hours. After automation: 5 minutes. For a team of 5 engineers, that's $10,000 per month saved. Braintrust is 10x faster than manual testing for regression detection. Contact us for a pipeline assessment—we'll prepare a dashboard with preliminary metrics. We provide comprehensive LLM evaluation metrics and automated model testing.

Our Process

1. Analysis — We get acquainted with your data, prompts, and quality metrics.
2. Design — Define test case set and scorers.
3. Implementation — Write integration code, set up CI/CD.
4. Testing — Run baseline, compare with current results.
5. Deployment — Hand over documentation, access, and team training.

What's Included in the Result

• Configured Braintrust project with your datasets and metrics.
• Integration with your CI/CD (GitHub Actions, GitLab CI, Jenkins).
• Automatic regression notifications.
• Documentation for adding new cases and metrics.
• Team training (2–3 hours).

Table: Braintrust vs Custom Solution

Table: Implementation Stages

For custom metrics, you can write your own scorers—we assist with typical cases.

Leave a request—we'll contact you within a day to discuss your project. We'll send example dashboards and explain how Braintrust will save your team dozens of hours per month. Get a consultation from an engineer with 5 years of ML experience—we'll assess your pipeline and propose the optimal solution.

MLOps: Infrastructure for Training, Deploying, and Monitoring ML Models

The model is trained, metrics — F1 0.94 on validation. Three months later in production, quality drops by 12%. No one knows when — there is no monitoring. It's impossible to retrain quickly — the training script is in a Jupyter notebook of a data scientist who has already left. Data for retraining is collected manually from three disparate systems. About half of the projects come to us with this pain. We build a turnkey MLOps platform: from experiment tracking to automatic deployment and data drift monitoring. We will assess your infrastructure in 1–2 weeks, and in 4–6 weeks you will get a basic MLOps core running in production. Our team has 10+ years of experience in ML infrastructure, over 50 implementations.

How does MLOps infrastructure benefit your ML projects?

Experiment Tracking and Reproducibility

Without tracking, an ML project turns into chaos: it's unclear which checkpoint is better, which hyperparameters were used, which dataset. Reproducing a result a month later is a quest.

Why is experiment tracking the foundation of reproducibility?

MLflow is an open source standard for tracking. It logs parameters, metrics, artifacts (models, graphs), and code. MLflow Model Registry is a centralized model storage with versioning and lifecycle stages (Staging → Production → Archived). Deployment via MLflow Serving or integration with external systems.

Typical initialization in code:

import mlflow

mlflow.set_experiment("fraud-detection-v2")
with mlflow.start_run():
    mlflow.log_params({"learning_rate": 3e-4, "batch_size": 64, "epochs": 10})
    mlflow.log_metric("val_f1", val_f1, step=epoch)
    mlflow.pytorch.log_model(model, "model")

This is the minimum. In production, we add logging of system metrics (GPU utilization, memory), dataset (hash, version), code (git commit hash). Weights & Biases — richer UI, collaboration features, sweep for hyperparameter optimization. MLflow — for on-premise deployment without external dependencies.

DVC (Data Version Control) — versioning of data and models on top of git. Data is stored in S3/GCS/Azure Blob, only metadata (hashes) in git. dvc repro reproduces the entire pipeline from raw data to metrics.

To ensure reproducibility of training, fix random seeds (torch.manual_seed, numpy.random.seed, random.seed) and record them in experiment metadata. Without this, debugging irregular results is painful. Log the dataset version (DVC hash) and git commit — then any experiment can be reproduced down to the byte.

Pipeline Orchestration: Kubeflow, Airflow, Prefect

A pipeline orchestrator becomes necessary when: A 100-line training script in cron is fine for simple tasks. But as soon as you have a multi-step pipeline (data loading → preprocessing → feature engineering → training → validation → deployment if quality above threshold), you need an orchestrator with retry logic, visualization, and alerts.

Kubeflow — Kubernetes-native orchestrator for ML (see Kubeflow). Each step is a Docker container. Supports parallel steps, conditional branches, artifacts between steps. Integrates with Katib (AutoML), KServe (serving), Feast (feature store).

Apache Airflow — more general DAG orchestrator. Wide ecosystem of operators (S3, Spark, DBT, Kubernetes). Easier to deploy if Airflow already exists in the company.

Prefect / Metaflow — less boilerplate. Prefect 2.x with @flow and @task decorators — quick start for small teams.

Typical training pipeline architecture on Kubeflow:

1. Data ingestion component — fetches data from S3/DB, validates schema via Great Expectations
2. Preprocessing component — transformations, normalization, train/val/test split
3. Training component — training on GPU, logging to MLflow
4. Evaluation component — metric calculation, comparison with baseline in Model Registry
5. Conditional deployment — deploy only if new model is better than current by >2% F1

Each component is a separate Docker image. Pipeline is versioned in git. Scheduled run (retraining once a week on new data) or manual.

Model Registry and Lifecycle Management

Model Registry is not just a checkpoint store. It is a centralized system that knows:

• Which model is currently in production (and with what metrics)
• History of all versions with training parameters
• Metadata: dataset, git commit, validation results
• Lifecycle stage: None → Staging → Production → Archived

MLflow Model Registry — standard. For enterprise — Vertex AI Model Registry (GCP), SageMaker Model Registry (AWS), Azure ML Model Registry.

Model promotion through stages: automatically move model to Staging after successful eval, then manual or automatic (during A/B test) promotion to Production. Rollback — switch to previous Production version in seconds.

Serving: From FastAPI to Triton Inference Server

Simple case. FastAPI + PyTorch/ONNX on one server — 80% of production ML deployments are exactly that. Sufficient for most tasks with load up to 100 req/s.

from fastapi import FastAPI
import onnxruntime as ort

app = FastAPI()
session = ort.InferenceSession("model.onnx", providers=["CUDAExecutionProvider"])

@app.post("/predict")
async def predict(request: PredictRequest):
    inputs = preprocess(request.text)
    outputs = session.run(None, {"input_ids": inputs})
    return {"label": postprocess(outputs)}

Triton Inference Server — production standard for high loads (500+ req/s). Dynamic batching, concurrent model execution, model ensemble. Supports TensorRT, ONNX, PyTorch TorchScript, TensorFlow SavedModel.

KServe — Kubernetes-native ML serving with autoscaling, canary deployments, A/B testing out of the box. Scale-to-zero for inactive models — savings on infrastructure up to 40% annually for a project with 10 models.

Monitoring: Data Drift, Model Drift, Infrastructure Metrics

Monitoring — what is usually done last and regretted first. Three levels.

Infrastructure monitoring. Latency (P50/P95/P99), throughput (req/s), error rate (4xx, 5xx), GPU/CPU utilization. Prometheus + Grafana — standard. Alert when P99 latency > threshold or error rate > 1%.

Data drift monitoring. Distribution of input data changes over time. Detect via PSI (Population Stability Index) for numerical features: PSI > 0.2 — strong drift. Chi-squared test for categorical, Kolmogorov-Smirnov test for continuous. Evidently AI — open source library with ready-made drift tests.

Model drift monitoring. If ground truth is delayed (e.g., we know conversion after a week) — monitor real metrics. If not — surrogate metrics: distribution of prediction scores, proportion of confident predictions.

Alerting. Three levels: INFO (minor drift, log it), WARNING (significant, notify team), CRITICAL (quality dropped below threshold — automatic switch to fallback model).

Why is data drift monitoring important?

Without it, you learn about model degradation only from user complaints or ringing SLA. A drift alert allows you to retrain the model in advance, before errors start causing losses. In one of our projects, PSI monitoring detected drift 2 days after a data source change — this saved the campaign.

Feature Store

Feature Store solves the training-serving skew problem. If preprocessing during training and inference is implemented in two different places — divergence is inevitable.

A Feature Store is needed when:

• Several models use the same features
• Features are computed from streaming data (real-time)
• Large team with different people on feature engineering and model training

Feast — open source Feature Store. Offline store (S3 + Parquet) for training, online store (Redis, DynamoDB) for low-latency inference. Feature definitions as code, materialization job syncs offline → online.

Tecton (commercial), Vertex AI Feature Store (GCP), SageMaker Feature Store (AWS) — managed options with less ops overhead.

CI/CD for ML

ML CI/CD is regular CI/CD plus specific ML steps.

ML-specific checks in CI:

• Reproducibility check: run training with a fixed seed, result must match
• Data validation: Great Expectations or Pandera on schema/distribution checks
• Model performance check: automatic eval on holdout, block merge if degradation > threshold
• Latency regression test: inference must meet SLA

GitOps for deployment. Merge to main → CI triggers training → eval → if passes → automatic deployment to Staging → smoke tests → manual promotion to Production or automatic upon successful canary.

Tools: GitHub Actions / GitLab CI for CI, ArgoCD for GitOps deployment on Kubernetes.

What's Included in MLOps Platform Development

We provide a full cycle of work, documentation, and team training.

Total time from audit to full MLOps platform: 3–5 months. Also possible phased launch: basic level (tracking + serving) in 4–6 weeks.

Cost is calculated individually based on data volume, number of models, and infrastructure requirements. Order an MLOps infrastructure audit — get a roadmap in 1–2 weeks. Contact us for a project assessment — we will send a preliminary estimate within 2 business days.

Note: warranty on architectural solutions — 12 months. We provide integration certificates with major cloud providers (AWS, GCP, Azure). During our work, we have not lost a single client after the first implementation — the experience of 50+ successful MLOps projects speaks for itself. Get a consultation on building an MLOps platform today.