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.