Eval-Driven Development (EDD) ensures AI systems behave correctly in production, moving beyond good demos to measurable quality.

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Why production AI needs more than a demo

Shipping AI without evaluations risks deploying broken systems that users discover first.

Shipping AI without evals is like deploying code without tests

Deploying AI without evaluations is comparable to releasing code without tests, leading to user-discovered breakage.

First wave of enterprise AI prototypes

The initial enterprise AI prototypes generated excitement with surprisingly good answers from simple chat windows and clever prompts.

Production reality arrived

Production reality revealed issues where prompt changes, cheaper models, retrieval updates, or safety guardrails caused problems.

Eval-Driven Development closes the gap

Eval-Driven Development addresses the gap between impressive demos and the realities of production AI behavior.

Better question for production AI

The key question for production AI is proving correct system behavior across workflows, risks, users, and constraints.

What is Eval-Driven Development

Eval-Driven Development (EDD) is a practice defining, running, analyzing, and improving evaluations throughout the AI application lifecycle.

Eval definition

An eval is a structured measurement of an AI system's performance for specific tasks regarding correctness, safety, and reliability.

Production evals are tailored

Unlike public benchmarks, production evaluations are customized to specific business contexts, including workflows, customers, and policies.

Evals need to be product-specific

Evaluations must be tailored to specific products to ensure relevance and effectiveness in measuring AI system performance.

TDD vs EDD for software development

Eval-Driven Development for AI extends Test-Driven Development principles to account for the probabilistic nature of AI systems.

Development Approach	Traditional Software (TDD)	AI Software (EDD)
Steps	Write tests, write code, run tests, refactor, repeat	Define quality, build scenarios, run AI system, grade outputs, analyze failures, improve system, gate production releases

Goal of EDD is measurable development

The primary objective of Eval-Driven Development is to make AI development quantifiable, not to achieve deterministic AI.

Traditional testing insufficient for AI

Traditional software tests are often deterministic, which is not suitable for the complex, probabilistic nature of AI systems.

Traditional tests are deterministic

Traditional software tests typically have a single expected answer and use binary assertions.

AI systems are different

AI systems often have multiple acceptable responses, depending on various contextual factors like policy and order history.

EDD is broader than unit testing

Eval-Driven Development encompasses more than unit testing by combining various methods to assess AI quality comprehensively.

TDD vs EDD for system behavior

TDD verifies if code works, while EDD confirms if the AI system behaves correctly, addressing their different natures.

Production eval stack components

A mature eval practice requires a system with specific components, not just a single spreadsheet or script.

1. Representative test dataset

The eval dataset must contain scenarios the AI system will encounter in production, including diverse cases.

2. Clear grading rubric

A rubric defines what constitutes "good," "acceptable," and "bad" performance for the AI system.

3. Right grading method

Each requirement should be graded using the most cost-effective and reliable method available.

Grading method suitability

Different grading methods are best suited for specific types of AI evaluations and carry distinct risks.

Grading method	Best for	Risk
Deterministic checks	JSON schema, exact labels, required fields, tool calls	Too brittle for open-ended answers
Human review	High-risk, regulated, subjective cases	Expensive and slower
LLM-as-judge	Semantic quality, tone, groundedness, completeness	Judge bias and inconsistency
Hybrid evaluation	Most production AI systems	Requires orchestration and calibration

Practical grading approach

A practical approach uses deterministic checks where possible, LLM judges for semantic quality, and human review for high-risk or disputed cases.

4. Full run reproducibility

Every eval run must record its exact configuration to ensure reproducibility and aid in debugging regressions.

5. Metrics that matter

Relying on one average score is insufficient; metrics should be tracked by workflow, risk tier, customer segment, and model version.

6. Shared failure taxonomy

A common failure taxonomy provides a shared language for teams to discuss and categorize AI quality issues.

EDD process workflow

A practical Eval-Driven Development workflow consists of ten sequential steps, from prototype to production.

Step 1: Define production goal

Clearly define the production goal, avoiding vague objectives.

e.g.A "build an AI assistant for support" goal is weak; a "support agent that resolves refund/replacement for eligible orders, follows policy, protects data, escalates exceptions, maintains p95 latency under 8 seconds, and stays within budget" is stronger.

Step 2: Identify critical workflows

Prioritize workflows based on their value and associated risks.

e.g.Workflows like "Order status" are high value/low risk, while "Request for another user’s data" is low value/critical risk.

Workflow value and risk table

Workflow value and risk prioritization ranges from high value/low risk (Order status) to low value/critical risk (Request for another user’s data).

Workflow	Value	Risk
Order status	High	Low
Refund eligibility	High	Medium
Replacement initiation	High	Medium
Payment dispute	Medium	High
Policy exception	Medium	High
Request for another user’s data	Low	Critical

Single global score insufficient

A single global score is inadequate because system readiness varies by workflow, for example, for order status versus payment disputes.

Step 3: Define success and failure criteria

Establish explicit thresholds for success and failure, which can evolve over time.

e.g.Criteria includes minimum task success rate (0.92), groundedness (0.95), tool-use accuracy (0.95), maximum p95 latency (8 seconds), and cost ($80 per 1000 tasks).

Step 4: Build initial eval dataset

Start with a small but realistic initial eval suite, expanding it as production usage increases.

Step 5: Create baseline

Run the current AI system against the eval suite to establish a baseline for performance.

Example baseline metrics

An example baseline shows metrics like task success rate, hallucination rate, and p95 latency.

Metric	Baseline
Task success rate	78%
Hallucination rate	9%
Tool-use accuracy	83%
Safety violation rate	1.2%
p95 latency	11.8 seconds
Cost per 1,000 tasks	$145

Baseline enables evidence-based improvement

A baseline allows teams to make improvements based on evidence rather than relying on intuition.

Step 6: Improve system iteratively

Use identified failure patterns to guide decisions on what aspects of the system to fix.

Failure pattern to likely fix

Various failure patterns, like hallucinations or wrong tool arguments, are associated with specific likely fixes.

Failure pattern	Likely fix
Hallucination	Better retrieval, stricter grounding prompt, citation requirement
Wrong tool	Improve tool descriptions, routing logic, examples
Wrong tool arguments	Stronger schema validation, deterministic checks
Incomplete answer	Add decomposition step or improve prompt rubric
Unsafe answer	Safety classifier, refusal examples, policy guardrails
High latency	Model routing, caching, fewer retrieval calls
High cost	Smaller model for low-risk cases, shorter context, batching

Eval suite guides next improvements

The eval suite not only grades the system but also indicates where the next improvements should be made.

Step 7: Add evals to CI/CD

Integrate evals into the CI/CD pipeline, triggering them for any changes to prompts, models, retrieval, tools, workflows, or guardrails.

Eval tiers and purpose

Different tiers of evaluations are triggered at various stages of development and deployment, each serving a specific purpose.

Tier	Trigger	Purpose
Smoke eval	Pull request	Fast check for obvious breakage
Regression eval	Merge to main	Compare with baseline
Full eval	Before staging or production	Release readiness
Safety eval	Before production and scheduled	Red-team and policy validation
Online eval	Production	Drift, incidents, feedback, canary quality

EDD part of engineering system

Integrating evals into CI/CD makes Eval-Driven Development an intrinsic part of the engineering system.

Step 8: Define release gates

Convert risk tolerance into specific engineering thresholds that must be met before release.

e.g.Release gates include overall task success rate (0.92), hallucination rate (0.02), tool-use accuracy (0.95), p95 latency (8 seconds max), cost ($80 per 1000 tasks max).

Step 9: Monitor production behavior

Supplement offline evals with production monitoring to capture real-world scenarios not covered by test sets.

Step 10: Turn failures into regression tests

Convert every significant production failure into a future eval case to strengthen the eval suite over time.

Evaluating AI agents requires process evals

For AI agents, evaluating outcomes alone is insufficient; process failures can occur even with correct final answers.

Chatbot vs agent distinction

A chatbot produces text, whereas an agent takes actions, a distinction that fundamentally changes evaluation needs.

Agent evaluation layers

Agents require evaluation at two layers: outcome evals and process evals.

Process evals non-negotiable for high-risk

Process evaluations are absolutely necessary for high-risk workflows, as a good final answer does not excuse policy violations.

How evals reduce AI cost

Evals provide evidence for cost optimization decisions, especially when considering replacing expensive frontier models with cheaper ones.

Best practices for EDD

Follow these best practices to effectively implement Eval-Driven Development.

Maturity model for EDD

Organizations typically progress through various levels of maturity in their Eval-Driven Development adoption.

EDD Maturity Levels

The EDD maturity model outlines five levels, from basic demo testing to continuous optimization, with clear next steps for progression.

Level	What it looks like	Next step
Level 0: Demo testing	A few hand-picked prompts, no rubric	Capture representative examples
Level 1: Manual evals	Spreadsheet review, small rubric	Convert cases to versioned JSONL or CSV
Level 2: Automated offline evals	Eval runner, basic metrics	Store run lineage and compare baselines
Level 3: CI/CD regression evals	Release gates block bad changes	Add trace-level agent evals and safety suites
Level 4: Production monitoring	Online traces, feedback, drift, incidents	Convert failures into evals automatically
Level 5: Continuous optimization	Cost-quality routing and fallback	Tune routing, models, prompts, and workflows continuously

Suggested EDD targets

Suggested EDD maturity targets vary by development stage, from Level 1 for prototypes to Level 5 for scaled AI platforms.

Leader questions before AI production

Leaders should ask specific questions before approving production AI to ensure readiness and accountability.

Final takeaway

Eval-Driven Development builds trust and confidence in AI systems by making their behavior measurable, just as TDD did for code.

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APEX

Eval-Driven Development for AI production

Eval-Driven Development (EDD) ensures AI systems behave correctly in production, moving beyond good demos to measurable quality.

Made with Rinto — analyse your own content free

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SECT

Why production AI needs more than a demo

Shipping AI without evaluations risks deploying broken systems that users discover first.

SUP

Shipping AI without evals is like deploying code without tests

Deploying AI without evaluations is comparable to releasing code without tests, leading to user-discovered breakage.

NTRL

First wave of enterprise AI prototypes

The initial enterprise AI prototypes generated excitement with surprisingly good answers from simple chat windows and clever prompts.

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EVID

Production reality arrived

Production reality revealed issues where prompt changes, cheaper models, retrieval updates, or safety guardrails caused problems.

DATA

Prompt change reduced accuracy

A prompt change improved tone but unfortunately reduced the system's accuracy.

DATA

Cheaper model missed edge cases

A cheaper model reduced cost, but it failed to address critical edge cases.

DATA

Retrieval update surfaced wrong policy

An update to the retrieval system resulted in the display of incorrect policy information.

DATA

Agent called tool with wrong parameter

An AI agent correctly called a tool but unfortunately used an incorrect parameter.

DATA

Safety guardrail blocked legitimate users

A safety guardrail inadvertently prevented legitimate users from accessing the system.

DATA

Customer found hallucination first

A customer discovered a hallucination before the engineering team identified it.

INSG

Eval-Driven Development closes the gap

Eval-Driven Development addresses the gap between impressive demos and the realities of production AI behavior.

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SUP

Better question for production AI

The key question for production AI is proving correct system behavior across workflows, risks, users, and constraints.

JUST

Evals essential for production

Evals are essential to verify correct system behavior across various critical aspects for production readiness.

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SECT

What is Eval-Driven Development

Eval-Driven Development (EDD) is a practice defining, running, analyzing, and improving evaluations throughout the AI application lifecycle.

SUP

Eval definition

An eval is a structured measurement of an AI system's performance for specific tasks regarding correctness, safety, and reliability.

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EVID

Production evals are tailored

Unlike public benchmarks, production evaluations are customized to specific business contexts, including workflows, customers, and policies.

JUST

Public benchmarks are insufficient

Public benchmarks indicate general model strength but cannot confirm specific business policy adherence or data exposure avoidance.

INSG

Evals need to be product-specific

Evaluations must be tailored to specific products to ensure relevance and effectiveness in measuring AI system performance.

CMPR

TDD vs EDD for software development

Eval-Driven Development for AI extends Test-Driven Development principles to account for the probabilistic nature of AI systems.

Development Approach	Traditional Software (TDD)	AI Software (EDD)
Steps	Write tests, write code, run tests, refactor, repeat	Define quality, build scenarios, run AI system, grade outputs, analyze failures, improve system, gate production releases

JUST

Goal of EDD is measurable development

The primary objective of Eval-Driven Development is to make AI development quantifiable, not to achieve deterministic AI.

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SECT

Traditional testing insufficient for AI

Traditional software tests are often deterministic, which is not suitable for the complex, probabilistic nature of AI systems.

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SUP

Traditional tests are deterministic

Traditional software tests typically have a single expected answer and use binary assertions.

EXMP

Calculator addition example

An example of a deterministic test is Assert.Equal(4, Calculator.Add(2, 2)), expecting one exact outcome.

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OPP

AI systems are different

AI systems often have multiple acceptable responses, depending on various contextual factors like policy and order history.

EXMP

Customer refund request example

A customer's refund request for defective headphones after 45 days demonstrates the complexity of AI responses.

EVID

Factors influencing best AI answer

The best AI answer depends on policy, order history, defect status, product category, warranty rules, escalation, tone, and safety.

EVID

Questions for useful AI eval

A useful AI evaluation must ask if the answer correctly explained policy, avoided invention, asked about defects, used right sources, called right tools, met limits, and escalated.

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INSG

EDD is broader than unit testing

Eval-Driven Development encompasses more than unit testing by combining various methods to assess AI quality comprehensively.

EVID

EDD methods combined

EDD combines deterministic checks, rubric scoring, LLM-as-judge, human review, trace analysis, statistical reporting, and production monitoring.

JUST

TDD vs EDD for system behavior

TDD verifies if code works, while EDD confirms if the AI system behaves correctly, addressing their different natures.

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SECT

Production eval stack components

A mature eval practice requires a system with specific components, not just a single spreadsheet or script.

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SUP

1. Representative test dataset

The eval dataset must contain scenarios the AI system will encounter in production, including diverse cases.

EVID

Dataset scenario types

Scenarios include happy-path, edge cases, adversarial prompts, historical failures, synthetic cases, multi-turn workflows, tool failures, and high-risk cases.

EXMP

Customer support agent dataset examples

For customer support, this includes refund questions, product defects, angry customers, privacy attacks, policy exceptions, and payment disputes.

EXMP

Claims processing agent dataset examples

For claims processing, examples include clean claims, incomplete documents, fraud indicators, policy exclusions, appeals, and conflicting evidence.

INSG

Dataset needs representativeness

A good eval dataset is effective because it is representative of real-world situations, not just large in size.

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SUP

2. Clear grading rubric

A rubric defines what constitutes "good," "acceptable," and "bad" performance for the AI system.

EVID

Common scoring dimensions

Common scoring dimensions include correctness, completeness, grounding, safety, privacy, compliance, tone, formatting, tool use, escalation, latency, and cost.

JUST

Rubric enables decision-making

A rubric allows teams to make objective decisions about AI quality, resolving subjective arguments.

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SUP

3. Right grading method

Each requirement should be graded using the most cost-effective and reliable method available.

CMPR

Grading method suitability

Different grading methods are best suited for specific types of AI evaluations and carry distinct risks.

Grading method	Best for	Risk
Deterministic checks	JSON schema, exact labels, required fields, tool calls	Too brittle for open-ended answers
Human review	High-risk, regulated, subjective cases	Expensive and slower
LLM-as-judge	Semantic quality, tone, groundedness, completeness	Judge bias and inconsistency
Hybrid evaluation	Most production AI systems	Requires orchestration and calibration

DCSN

Practical grading approach

A practical approach uses deterministic checks where possible, LLM judges for semantic quality, and human review for high-risk or disputed cases.

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SUP

4. Full run reproducibility

Every eval run must record its exact configuration to ensure reproducibility and aid in debugging regressions.

EVID

Configuration details to record

Configuration includes model provider/version, temperature, prompt/retrieval index/tool schema versions, orchestration logic, safety policies, code commit, dataset/grader version.

JUST

Debugging regressions requires reproducibility

Without the ability to reproduce an eval run, teams cannot confidently debug regressions in AI systems.

INSG

AI equivalent of build lineage

Reproducibility in AI evals is considered the AI equivalent of build lineage in traditional software development.

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SUP

5. Metrics that matter

Relying on one average score is insufficient; metrics should be tracked by workflow, risk tier, customer segment, and model version.

EVID

Useful metrics to track

Useful metrics include task success rate, hallucination rate, groundedness, tool-use accuracy, safety violation rate, privacy leakage rate, refusal quality, human escalation rate, p50 and p95 latency, cost per task, and confidence intervals.

INSG

Segmentation exposes risk

Averages conceal risks, while segmentation effectively reveals them by breaking down performance.

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SUP

6. Shared failure taxonomy

A common failure taxonomy provides a shared language for teams to discuss and categorize AI quality issues.

EVID

Useful failure categories

Categories include hallucination, wrong tool selection, wrong tool input, tool output misuse, incomplete answer, missing citation, unsafe response, privacy leak, policy violation, poor escalation, over/under-refusal, bad formatting, timeout, and looping.

INSG

Precision enables engineering fixes

Precise feedback, like an agent passing an ungrounded customer ID in a refund workflow, allows engineering teams to implement targeted fixes.

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SECT

EDD process workflow

A practical Eval-Driven Development workflow consists of ten sequential steps, from prototype to production.

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STEP

Step 1: Define production goal

Clearly define the production goal, avoiding vague objectives.

JUST

Stronger goals are measurable

The stronger, more detailed goal definition is valuable because it makes the objective measurable.

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STEP

Step 2: Identify critical workflows

Prioritize workflows based on their value and associated risks.

e.g.Workflows like "Order status" are high value/low risk, while "Request for another user’s data" is low value/critical risk.

DATA

Workflow value and risk table

Workflow value and risk prioritization ranges from high value/low risk (Order status) to low value/critical risk (Request for another user’s data).

Workflow	Value	Risk
Order status	High	Low
Refund eligibility	High	Medium
Replacement initiation	High	Medium
Payment dispute	Medium	High
Policy exception	Medium	High
Request for another user’s data	Low	Critical

INSG

Single global score insufficient

A single global score is inadequate because system readiness varies by workflow, for example, for order status versus payment disputes.

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STEP

Step 3: Define success and failure criteria

Establish explicit thresholds for success and failure, which can evolve over time.

e.g.Criteria includes minimum task success rate (0.92), groundedness (0.95), tool-use accuracy (0.95), maximum p95 latency (8 seconds), and cost ($80 per 1000 tasks).

DATA

Critical failures example

Critical failures specify maximum policy violations (0), privacy leaks (0), and unsafe completions (0).

JUST

Criteria must be explicit

The criteria must be explicit to ensure clear definitions, even if the specific numbers change.

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STEP

Step 4: Build initial eval dataset

Start with a small but realistic initial eval suite, expanding it as production usage increases.

EVID

Initial suite composition

A useful initial suite includes 50 happy-path examples, 50 edge cases, 25 adversarial cases, 25 historical failures, 25 tool-use workflows, and 25 safety/compliance cases.

SUBS

Add real user traces and incidents

As production usage grows, incorporate real user traces and examples derived from incidents into the dataset.

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STEP

Step 5: Create baseline

Run the current AI system against the eval suite to establish a baseline for performance.

CMPR

Example baseline metrics

An example baseline shows metrics like task success rate, hallucination rate, and p95 latency.

Metric	Baseline
Task success rate	78%
Hallucination rate	9%
Tool-use accuracy	83%
Safety violation rate	1.2%
p95 latency	11.8 seconds
Cost per 1,000 tasks	$145

JUST

Baseline enables evidence-based improvement

A baseline allows teams to make improvements based on evidence rather than relying on intuition.

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STEP

Step 6: Improve system iteratively

Use identified failure patterns to guide decisions on what aspects of the system to fix.

CMPR

Failure pattern to likely fix

Various failure patterns, like hallucinations or wrong tool arguments, are associated with specific likely fixes.

Failure pattern	Likely fix
Hallucination	Better retrieval, stricter grounding prompt, citation requirement
Wrong tool	Improve tool descriptions, routing logic, examples
Wrong tool arguments	Stronger schema validation, deterministic checks
Incomplete answer	Add decomposition step or improve prompt rubric
Unsafe answer	Safety classifier, refusal examples, policy guardrails
High latency	Model routing, caching, fewer retrieval calls
High cost	Smaller model for low-risk cases, shorter context, batching

JUST

Eval suite guides next improvements

The eval suite not only grades the system but also indicates where the next improvements should be made.

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STEP

Step 7: Add evals to CI/CD

Integrate evals into the CI/CD pipeline, triggering them for any changes to prompts, models, retrieval, tools, workflows, or guardrails.

CMPR

Eval tiers and purpose

Different tiers of evaluations are triggered at various stages of development and deployment, each serving a specific purpose.

Tier	Trigger	Purpose
Smoke eval	Pull request	Fast check for obvious breakage
Regression eval	Merge to main	Compare with baseline
Full eval	Before staging or production	Release readiness
Safety eval	Before production and scheduled	Red-team and policy validation
Online eval	Production	Drift, incidents, feedback, canary quality

INSG

EDD part of engineering system

Integrating evals into CI/CD makes Eval-Driven Development an intrinsic part of the engineering system.

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STEP

Step 8: Define release gates

Convert risk tolerance into specific engineering thresholds that must be met before release.

e.g.Release gates include overall task success rate (0.92), hallucination rate (0.02), tool-use accuracy (0.95), p95 latency (8 seconds max), cost ($80 per 1000 tasks max).

DATA

Critical safety release gates

Critical safety gates require maximum PII leakage (0), critical policy violations (0), and harmful completions (0).

DATA

Regression release gates

Regression gates limit maximum task success drop (0.01) and groundedness drop (0.01) versus baseline.

JUST

Failure to meet gates blocks shipment

The rule is straightforward: if the system fails to meet the release gate criteria, it will not be shipped.

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STEP

Step 9: Monitor production behavior

Supplement offline evals with production monitoring to capture real-world scenarios not covered by test sets.

EVID

Production monitoring captures

Monitoring captures live traces, tool calls, retrieved context, user feedback, human escalations, safety triggers, latency/cost spikes, failure categories, topic/document drift, and incident reviews.

INSG

Production is source of best eval cases

Production is not the final stage of evaluation; it is the source of the most valuable eval cases.

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STEP

Step 10: Turn failures into regression tests

Convert every significant production failure into a future eval case to strengthen the eval suite over time.

SUBS

Failure loop

The loop involves identifying a production issue, incident review, root cause analysis, adding an eval case, fixing the system, rerunning the suite, and gating future releases.

INSG

Eval suite becomes smarter

This iterative process ensures the evaluation suite continuously improves and becomes more effective over time.

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SECT

Evaluating AI agents requires process evals

For AI agents, evaluating outcomes alone is insufficient; process failures can occur even with correct final answers.

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SUP

Chatbot vs agent distinction

A chatbot produces text, whereas an agent takes actions, a distinction that fundamentally changes evaluation needs.

EVID

Production agent actions

A production agent may retrieve documents, call APIs, update records, create tickets, ask questions, escalate, and decide when to stop.

WARN

Only evaluating final answer is risky

Evaluating only the final answer risks missing dangerous process failures, even if the outcome appears correct.

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EVID

Agent evaluation layers

Agents require evaluation at two layers: outcome evals and process evals.

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SUP

Outcome evals

Outcome evals assess the quality of the final result produced by the agent.

EXMP

Outcome eval examples

Examples include: Was the task completed? Was the answer correct/grounded? Was intent resolved? Did it escalate as needed?

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SUP

Process evals

Process evals examine whether the agent followed the correct operational path.

EXMP

Process eval examples

Examples include: Did it select the right tool? Were tool inputs valid? Did it avoid unnecessary tools? Did it use outputs correctly? Did calls succeed? Did it loop/stall? Did it follow workflow sequence?

REQ

Process evals non-negotiable for high-risk

Process evaluations are absolutely necessary for high-risk workflows, as a good final answer does not excuse policy violations.

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SECT

How evals reduce AI cost

Evals provide evidence for cost optimization decisions, especially when considering replacing expensive frontier models with cheaper ones.

JUST

Cost optimization answer not from demo

Decisions about replacing expensive frontier models with cheaper ones should be based on evals, not just demos.

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SUP

Four common cost reduction strategies

There are four common strategies to reduce AI costs using evaluations.

EVID

1. Full replacement

Use a cheaper model only if it consistently meets the same production thresholds as the expensive one.

EVID

2. Routing

Route easy, low-risk tasks to a cheaper model, while sending complex or high-risk tasks to a stronger model.

EVID

3. Fallback

Allow a cheaper model to attempt the task first, escalating to a stronger model or human if confidence is low or risks are detected.

EVID

4. Distillation or prompt optimization

Improve smaller model behavior using high-quality outputs from stronger models, then verify these improvements with evaluations.

JUST

Evals enable cost optimization without guessing

Evaluations enable teams to optimize costs effectively without making blind decisions or guessing about performance.

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SECT

Best practices for EDD

Follow these best practices to effectively implement Eval-Driven Development.

TIP

Start with real user journeys

Base evals on specific workflows, policies, customers, documents, tools, and risks, not generic model benchmarks.

TIP

Start small but representative

A small, representative 50-case eval suite addressing common failure modes is better than a large one with many easy examples.

TIP

Separate low-risk and high-risk workflows

Avoid a single global score; a model may be sufficient for FAQ lookups but inadequate for claims adjudication.

TIP

Evaluate traces, not just final answers

For agents, trace evaluation is crucial as bugs often reside in tool calls, parameters, retrieval results, and intermediate decisions.

TIP

Use deterministic checks wherever possible

Automate machine-checkable requirements such as JSON validity, schema compliance, exact labels, tool arguments, SQL execution, code tests, and citation fields.

TIP

Calibrate LLM judges

LLM-as-judge is useful but not perfect, requiring calibration against human labels and recalibration when prompts, rubrics, or models change.

TIP

Track quality, latency, and cost together

An AI system can be accurate but still fail in production if it's too slow or too expensive, emphasizing the need for comprehensive tracking.

TIP

Turn every incident into an eval

The most effective eval cases often emerge from real failures, making every incident an opportunity to strengthen the suite.

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SECT

Maturity model for EDD

Organizations typically progress through various levels of maturity in their Eval-Driven Development adoption.

CMPR

EDD Maturity Levels

The EDD maturity model outlines five levels, from basic demo testing to continuous optimization, with clear next steps for progression.

Level	What it looks like	Next step
Level 0: Demo testing	A few hand-picked prompts, no rubric	Capture representative examples
Level 1: Manual evals	Spreadsheet review, small rubric	Convert cases to versioned JSONL or CSV
Level 2: Automated offline evals	Eval runner, basic metrics	Store run lineage and compare baselines
Level 3: CI/CD regression evals	Release gates block bad changes	Add trace-level agent evals and safety suites
Level 4: Production monitoring	Online traces, feedback, drift, incidents	Convert failures into evals automatically
Level 5: Continuous optimization	Cost-quality routing and fallback	Tune routing, models, prompts, and workflows continuously

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REQ

Suggested EDD targets

Suggested EDD maturity targets vary by development stage, from Level 1 for prototypes to Level 5 for scaled AI platforms.

DATA

Prototype EDD target

A prototype should aim for a minimum of Level 1 maturity in Eval-Driven Development.

DATA

Internal pilot EDD target

An internal pilot should target a minimum of Level 2 maturity in Eval-Driven Development.

DATA

External production EDD target

External production systems require a minimum of Level 3 maturity in Eval-Driven Development.

DATA

Regulated/high-risk production EDD target

Regulated or high-risk production systems need a minimum of Level 4 maturity in Eval-Driven Development.

DATA

Scaled AI platform EDD target

A scaled AI platform should aim for a Level 5 maturity target in Eval-Driven Development.

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SECT

Leader questions before AI production

Leaders should ask specific questions before approving production AI to ensure readiness and accountability.

EVID

Key questions for leaders

Leaders should inquire about top workflows, eval dataset size/coverage, failure severity, pass rates by workflow/risk tier, comparison to baseline, hallucination rate, tool-use accuracy, safety/privacy evals, p95 latency, cost per task, human escalation rate, release gates, monitoring, and incident-to-regression-test process.

JUST

Unanswered questions mean not production-ready

If a team cannot answer these questions, the AI system is not considered ready for production deployment.

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SECT

Final takeaway

Eval-Driven Development builds trust and confidence in AI systems by making their behavior measurable, just as TDD did for code.

INSG

EDD is not bureaucracy

Eval-Driven Development is a method for AI teams to move quickly without pretending the system is deterministic.

EVID

Demos create excitement, evals create trust

Demos generate initial excitement, but evaluations are what ultimately build trust in AI systems.

JUST

EDD provides discipline for probabilistic systems

EDD offers AI teams the same discipline as TDD for probabilistic systems: define, measure, analyze, improve, and block regressions.

INSG

Strongest feedback loop wins

Teams with the most robust feedback loops, not the flashiest demos, will ultimately succeed with production AI.

SUP

Shipping AI without evals is like deploying code without tests

Deploying AI without evaluations is comparable to releasing code without tests, meaning you won't know what broke until users do.

INSG

EDD reveals breakage before users

Eval-Driven Development ensures that AI teams discover system breakage before users encounter it.