What is eval-driven development: How to ship high-quality agents without guessing

The hardest part of building with LLMs is knowing whether your last change made things better or worse. A developer tweaks a system prompt, eyeballs a few outputs, and ships it. Two days later, a user reports that the agent stopped handling refund requests correctly. Most of the next day goes to bisecting changes to figure out which edit broke it.

Eval-driven development (EDD) solves this by giving you a reliable signal. Define what good output looks like, encode those definitions as evaluations, and use the eval scores as your oracle. If the eval correctly captures quality, then improving your score means improving your product. Every prompt tweak, model swap, and pipeline change becomes a measurable experiment with a clear outcome: score went up, or score went down.

Regression protection falls out of this naturally. When every change runs through the same eval suite before shipping, regressions surface in development instead of in production. But honestly, regression prevention is a side effect. The real value is upstream: EDD gives engineers a concrete optimization target where, before, they had subjective judgment and gut checks.

This guide covers what eval-driven development means in practice, how the eval-as-oracle model works, and what infrastructure teams need to run EDD at production scale.

What is eval-driven development?

Eval-driven development is a methodology where evaluations serve as the working specification for LLM-powered applications. Before you modify a prompt or swap a model, you define quality criteria and test every change against those criteria before it reaches production. Evaluation sits at the beginning of the development cycle, where it shapes decisions, rather than at the end, where it can only confirm or reject them.

If you've worked with test-driven development (TDD), the structure feels familiar. But TDD tests return pass or fail based on an exact match. EDD scores outputs across multiple quality dimensions because a response can be factually accurate but too long, or well-formatted but missing key information. A single binary check can't capture that.

Four properties separate EDD from the ad hoc evaluation most teams default to:

Evals as specifications. The eval suite defines what the application should do at any given point. When a product requirement changes, that change flows into evaluation criteria first, and the updated criteria drive optimization.

Dataset and run lineage. Each eval run is tied to a specific dataset version, prompt version, and model configuration. Teams can reproduce any prior result exactly and debug regressions weeks or months after a change shipped.

Judge calibration. When LLM-as-a-judge scoring is used, the judge needs regular calibration against human ratings. Without it, scoring drift inflates or deflates results over time, and your eval scores quietly stop reflecting actual quality.

Regression gates. Eval scores serve as promotion criteria between environments. A prompt change that drops accuracy beyond a defined threshold on the golden dataset gets blocked from staging or production automatically.

Why evals work as an optimization target

Here's the core insight: if your eval correctly captures what "good" means, then optimizing against it is sufficient. Everything else follows from that.

Say you're building a document summarization agent. You decide summaries should preserve key entities from the source material, stay under 200 words, and remain factually accurate. You encode those three criteria as evals with clear scoring rubrics. Now every change you make gets measured against the same bar, whether it's a new system prompt, a different model, or an adjusted retrieval pipeline. Each time, you get a quantified answer: did the scores go up or down?

This shifts where the hard problem lives. Instead of asking "is this output good?" after every change (a subjective question that three engineers will answer three different ways), you ask "is my eval right?" once. Eval calibration is a solvable, bounded problem, and it gets easier over time as you add production examples to your datasets and compare automated scores against human annotations.

Judge calibration and golden sets exist in EDD to keep the oracle trustworthy. A well-calibrated eval suite means you can trust the signal, and trusting the signal means you can move fast.

How eval-driven development differs from traditional LLM testing

Most teams test LLM applications with a handful of manual examples, maybe combined with automated checks for obvious failures like empty responses or format violations. Manual spot-checking catches broken outputs but doesn't give you consistent measurement across quality dimensions over time.

The eval-driven development lifecycle: From evals to production

EDD operates as a continuous three-phase loop, with each phase feeding into the next.

Phase 1: Define evals as specifications

You start by translating business requirements into measurable evaluation criteria. A team building a document summarization agent might define evals for factual accuracy against source material, summary length, preservation of key entities, and readability for the intended audience. All of these should be grounded in real production data, not hypothetical test cases.

Golden sets are curated collections of inputs paired with approved reference outputs that form the regression baseline for every change.

Eval suites organize related evaluations by feature or quality dimension, allowing targeted testing when a modification affects only part of the system.

Regression thresholds define acceptable score differences between the current production baseline and a proposed update. A prompt change that improves tone by 5% but reduces accuracy by 2% may pass under defined thresholds, while a larger accuracy drop would block deployment.

Human review supports automated scoring for subjective dimensions. Annotators periodically label sampled outputs, and teams use those labels to recalibrate automated judges so scoring stays accurate against real quality expectations.

Phase 2: Optimize against evals

Once evals are in place, they become your feedback loop for every change.

A developer sits down to improve response quality. They change the system prompt, run the eval suite in Braintrust's playground, and within minutes see the results: accuracy went up 3%, tone held steady, completeness dropped 1%. Net win. Ship it.

Without evals, that same developer would manually review dozens of outputs and make a gut call. Maybe they'd spot the accuracy improvement. They'd almost certainly miss the small completeness dip. With evals, the tradeoff is quantified and visible before anything reaches users.

In practice, this is what the daily rhythm looks like: a team building a customer support agent swaps to a newer model and notices their tone score dropped from 0.85 to 0.72 in the first eval run. Obvious regression. They adjust the system prompt to compensate, rerun, and see tone recover to 0.88 without affecting accuracy. Total time: 20 minutes. Without eval infrastructure, that tone regression might surface through customer complaints two weeks later.

Prompt edits, model swaps, few-shot updates, and retrieval adjustments all follow the same pattern: change, measure, decide. The eval suite treats every modification the same way, regardless of what changed under the hood.

Phase 3: Refine evals as business criteria change

User behavior shifts over time, requirements evolve, and new failure modes appear that your original evals didn't anticipate. Eval suites need continuous updates to keep pace.

Production traces that reveal new edge cases get added to golden sets, expanding coverage for future iterations. Judge rubrics are updated when priorities change (when brevity becomes more important than completeness, for example). Regression thresholds may tighten as the application matures and tolerance for quality variation decreases. Production signals then shape the next round of evaluation criteria used in development, and the loop continues.

Release management and eval gates

In eval-driven development, eval thresholds decide whether a change is eligible for release. No vibes-based approvals.

Environment promotion with evaluation gates. Changes move through development, staging, and production based on defined evaluation thresholds at each stage. During development, updates are tested against a smaller subset of the eval suite for faster iteration. Promotion to staging requires running the full evaluation suite on the complete golden dataset. Before reaching production, additional safety or compliance evaluations may run alongside the regression suite. If any metric falls below its threshold at any gate, the CI pipeline blocks the change automatically.

Canary deployments scored by evals. After a change passes staging, teams can route a portion of live traffic to the updated version and score both versions using the same evaluation criteria. Instead of relying only on operational metrics like latency or error rates, canary deployments measure output quality directly across real user interactions: accuracy, tone, completeness, and safety.

Rollback triggers. Production evaluation scores can trigger alerts or automatic rollbacks when quality drops below thresholds. If a model provider pushes an update that alters output behavior, monitoring evaluations detect the quality drop and either revert the system to a previous configuration or notify the team for review.

Audit trails and access controls. Every evaluation run, score update, and promotion decision is logged with timestamps and reviewer identity. Role-based access controls define who can modify evaluation criteria, approve promotions, or override regression gates.

Operational infrastructure for eval-driven development at scale

Running EDD at scale requires prompt management tooling, reproducibility guarantees, and judge calibration infrastructure alongside the eval suite.

Prompt templates and variables separate prompt logic from configuration, so teams can modify behavior by changing variable values rather than rewriting entire prompts. A template with variables for tone, response length, and domain context lets developers test variations through controlled experiments rather than ad hoc text edits.

Model and provider changes are treated as configuration updates that require evaluation. When a provider releases a new model version, the eval suite assesses its impact across all defined quality dimensions before any migration.

Reproducible reruns depend on version-pinning every component involved in an evaluation: the dataset, prompt template, model identifier, judge configuration, and scoring rubric. If a regression appears later, the team can rerun the exact evaluation that previously approved the change and determine what shifted.

Judge drift monitoring keeps LLM-based scorers aligned with human judgment over time. Teams compare automated scores against periodic human annotations and recalibrate when misalignment appears. Without monitoring, eval scores can gradually lose reliability even when thresholds appear to be met.

Offline evals validate changes before deployment, and production monitoring runs a subset of the same evals against live traffic to detect quality degradation in real time. Because both use shared scoring logic and thresholds, quality definitions stay consistent from development through production.

Why Braintrust is the best choice for eval-driven development

Most teams that attempt EDD end up stitching together separate tools for evaluation, CI gating, and production monitoring. What happens next is predictable: the eval definitions drift apart across those tools, and within a few months, the eval running in CI no longer matches what's being measured in production.

Braintrust keeps everything in one place. The eval you build during development is the same eval that gates your releases and monitors production quality. Shared definitions across every stage are what make the oracle model actually work.

During development, teams run experiments against versioned datasets and compare prompt or model variations using quantitative scores. Each experiment records full lineage: dataset version, prompt version, model configuration, and judge settings. So when a regression appears months later, you can reproduce the exact conditions that approved the original change and figure out what went wrong.

Braintrust integrates with CI/CD through a native GitHub Action that runs eval suites on every pull request. When metrics fall below thresholds, the change is blocked. Detailed reports identify which cases regressed and by how much.

Once changes reach production, the same scoring logic runs against live traffic at configurable sampling rates. Dashboards track quality metrics over time, and alerts fire when scores cross thresholds.

One thing worth calling out: product managers and engineers work within the same system. PMs review outputs and refine evaluation criteria in Braintrust's playground, while engineers run those same evaluations programmatically through the SDK and wire them into CI. Because both sides share the same datasets, scorers, and experiment records, a PM updating eval criteria in the morning means engineers are testing against the new criteria that afternoon.

Loop, Braintrust's built-in AI assistant, accelerates eval setup by analyzing production traces and generating evaluation datasets from them. When you're building your oracle, Loop helps you get to a calibrated eval suite faster.

Organizations including Notion, Stripe, Vercel, Zapier, and Ramp use Braintrust to run eval-driven development and ship AI changes with measurable quality validation.

Get started with Braintrust for free and make eval-driven development your default shipping workflow.

Ship with confidence when your eval is right

If your eval is right, every decision becomes simple: does the score go up or down? A single, trusted quality signal is what turns EDD from a nice idea into a production discipline you can actually rely on.

Braintrust gives you the infrastructure to build that eval, calibrate it against human judgment, gate your releases on it, and monitor it in production. Get started with Braintrust today and ship LLM changes with measurable validation at every stage.

FAQs about eval-driven development

What is eval-driven development (EDD)?

Eval-driven development is a release discipline for LLM systems. Teams define what good output looks like, encode those definitions as evaluations, and use eval scores as the source of truth for whether changes improve the product. Think of the eval as an oracle: if it correctly captures quality, improving your score means improving your product. Every prompt or model update must meet defined quality standards before deployment, and evaluations score quality across multiple dimensions rather than relying on binary pass/fail checks.

How is eval-driven development different from traditional LLM testing?

Traditional LLM testing relies on a small set of fixed examples reviewed after development, with binary pass/fail results. Eval-driven development embeds structured evaluation directly into the development process. Changes are measured across multiple quality dimensions, release decisions depend on defined thresholds, and datasets are updated as real-world usage exposes new behaviors.

What are eval gates, and how do they prevent regressions?

Eval gates are automated checks in the deployment pipeline that block a change if its evaluation scores fall below defined thresholds. If a prompt update reduces accuracy on the golden dataset beyond the allowed limit, it cannot move to staging or production. By enforcing thresholds automatically, eval gates catch regressions that manual review would miss.

What tools support eval-driven development workflows?

Running EDD requires coordinated support for dataset management, multi-dimensional scoring, release controls, and production monitoring. Braintrust provides these capabilities within a unified system, so teams can manage evaluation criteria, enforce promotion thresholds, and track production quality without maintaining separate infrastructure for each function.

How do I get started with eval-driven development?

Start by deciding which quality dimensions are most relevant to your application (accuracy, tone, completeness, safety, or others) and define clear scoring rules for each. Build a small set of representative test cases, set acceptable performance thresholds, and require every change to pass those thresholds before release. Braintrust supports this workflow with versioned datasets, automated scoring, CI enforcement, and production monitoring within the same system.

How do I know if my evals are good enough?

A well-calibrated eval produces scores that correlate with human judgment. Start by having annotators rate a sample of outputs, then compare those ratings against your automated eval scores. If the automated scores consistently agree with human reviewers, the eval is trustworthy enough to gate releases on. Over time, add production edge cases to your golden dataset and recalibrate judges periodically. Braintrust tracks scorer performance against human annotations and surfaces misalignment when it appears, so you know when your oracle needs tuning.