Braintrust is an enterprise-grade AI evaluation platform that integrates LLM quality testing directly into the development and CI/CD workflow — providing a dataset management system, prompt playground, and automated regression testing framework that treats "did this prompt change break my use case?" as a first-class engineering question with a quantitative answer.

What Is Braintrust?

• Definition: A commercial AI evaluation and observability platform (founded 2023) that combines logging, dataset management, prompt experimentation, and automated evaluation into a unified workflow — enabling engineering teams to apply the same rigor to LLM quality as they apply to software testing.
• CI/CD Integration: Braintrust evaluations run as code — Python or TypeScript eval scripts that execute in CI pipelines, compare results against a baseline score, and fail the build if quality regresses beyond a threshold.
• Dataset Versioning: Test cases are stored as versioned datasets — curated from production logs, hand-labeled examples, or synthetic data — and every evaluation run is linked to the exact dataset version used.
• Scoring System: Define custom scoring functions (exact match, semantic similarity, LLM-as-judge, human review) that evaluate any aspect of your application's output quality.
• Prompt Playground: Iterate on prompts against your dataset in a browser UI, see scores update in real-time, and promote the best version to production with full audit trail.

Why Braintrust Matters

• Catching Regressions Before Production: When a developer changes a system prompt to fix one issue, Braintrust runs the full evaluation suite and alerts if other use cases degrade — preventing the "fix one thing, break another" cycle that plagues LLM application development.
• Evidence-Based Decisions: Model upgrades (e.g., GPT-4o-mini → GPT-4o) are evaluated quantitatively across your actual use cases before committing — cost/quality tradeoffs become data-driven decisions.
• Production Data Loop: Real user interactions are automatically logged and can be curated into test cases — the evaluation dataset grows organically from production usage, continuously covering new edge cases.
• Multi-Metric Evaluation: A single LLM response can be scored simultaneously on accuracy, groundedness, safety, tone, and latency — giving a multi-dimensional view of quality changes.
• Enterprise Readiness: SOC 2 compliant, SSO support, team permissions, and audit logs — meets enterprise security requirements for regulated industries.

Core Braintrust Workflow

Defining an Evaluation:

import braintrust
from braintrust import Eval

async def my_task(input):
    response = openai.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": input["question"]}]
    )
    return response.choices[0].message.content

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

Eval(
    "Customer Support QA",
    data=[{"input": {"question": "What is your return policy?"}, "expected": "30-day returns"}],
    task=my_task,
    scores=[accuracy_scorer]
)

Running in CI:

braintrust eval my_eval.py --threshold 0.85
# Fails CI if average score drops below 85%

Key Braintrust Features

Logging:

• Wrap any LLM call with braintrust.traced() to capture inputs, outputs, latency, tokens, and cost.
• Every production request is logged and searchable — find the exact trace behind a user complaint.

Experiments:

• Compare two prompt versions side-by-side with statistical significance testing.
• "Version B is 12% more accurate than Version A with p < 0.05" — confidence before deployment.

Datasets:

• Build test suites from production logs, manual curation, or synthetic generation.
• Version datasets separately from code — reproduce any historical evaluation exactly.

Human Review:

• Route uncertain cases to human reviewers in the Braintrust UI.
• Collect human labels that improve automated scorer calibration over time.

Braintrust vs Alternatives

Braintrust is the evaluation platform that makes LLM quality regression testing as reliable and automated as unit testing in traditional software development — for engineering teams that need quantitative answers to "did this change make my AI worse?", Braintrust provides the infrastructure to catch quality regressions before they reach users.