Braintrust
Braintrust is an evaluation and observation platform for LLM applications providing datasets, automatic and model-based scoring functions, experiment comparison and integration into delivery pipelines.
Braintrust is a platform for systematically evaluating AI output: test datasets, scoring functions, version comparison and quality gates in delivery. It turns "feels better" into a number.
Explanation
Without evaluation, every prompt change is a gut feeling. The routine is always the same: a dataset of real inputs, a definition of "good", one run per variant, a comparison. Braintrust holds these parts together and can wire the comparison into delivery — a change that breaks the quality threshold does not ship. The hard part stays with the team: defining what "good" means for the specific use case.
Marketing Relevance
For decision makers, evaluation is where AI work becomes verifiable. Without measurement there is no defensible statement about improvement, no acceptance basis and no evidence for the department or audit. With measurement, an experiment becomes a product with a quality target.
Example
For the website chat, a dataset of 200 real questions with expected sources is created. Every prompt change runs against it; scoring covers source fidelity, completeness and tone. Only when all three hold does the change go live.
Common Pitfalls
A model as judge has its own weaknesses and occasionally favours phrasing that merely sounds convincing. That is why the dataset needs a human-reviewed core. Second mistake: filling test data with customer data without a legal basis and deletion concept.
Origin & History
Braintrust emerged in 2023/2024 as the realisation set in that LLM applications are unmaintainable without test procedures. Model-based scoring — one model grading another's output — moved in that period from stopgap to established practice, with all the known caveats.
Further Resources
Marketing Use Cases
Scoring chat and support answers against expected sources.
Comparing models and prompts before a switch.
Quality gate in delivery for AI features.
Frequently Asked Questions
Braintrust or Langfuse?
Langfuse is strong in tracing and self-hostable, Braintrust focuses on experiments and quality gates. Many teams keep tracing and evaluation separate anyway.
How many test cases do we need?
Fewer than you think, but real ones: 50 to 200 genuine requests with a clearly defined expected result say more than a thousand invented ones.
Related Services
Go deeper: Agentic AI Hub · Model comparison 2026