Slides: Evals 101 — Doug Guthrie, Braintrust

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Evals 101 — Doug Guthrie, Braintrust

Relationship To World's Fair 2026

These slides are extracted from a public AI Engineer YouTube video connected to World's Fair 2026. Speaker-matched clips are supporting context unless later confirmed as exact session recordings; official livestream recordings are day-level/event-level source material.

Related Scheduled Sessions

Extracted Slides

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Braintrust at a glance

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Why evals? Evals help answer questions

Model selection How Gets Al Cost efficiency

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best choice for our diverse real-world performance without

needs? “nee excessive costs?

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reflect our compan fs “Are we learning from “How do we know

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How evals can help your business

Cut dev time Rapid heraton cycies & local tested on multiple LLMs seamlessly

Reduce costs Autontated evals replace manuel review alowing faster eration ¢ release

Enhance quality Real time nonttoring & compliance to redcce risk and improve Cx

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What is an Eval?

Definition: An Eval (short for evaluation) is a

structured test that checks how well your Al

system performs. It helps you measure

quality, reliability, and correctness across

scenarios.

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3 Ingredients in an Eval

DATASET SCORER

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There are two eval mental models

Offline Evals

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What should I improve?

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Datasets - Tips

Cees) Te apcvb arses Tae codes 1 Con

focus on building a feedback loop rather than

a perfect dataset

e §=Never Stop Iterating:

Use Logs to capture more edge cases and

create more holistic Evals

Cas occ ea lee Lelpe TAM Sale Ee

use human review to establish ground truth

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slide-017.jpg

OCR text:

BRAINTRUST.DEV

Scorer Types

Code-basedscorers

LLM-as-a-judgescorers

Exactorbinaryconditions

Subjectiveorcontextualfeedback

AIE

Numericcomparisons

Human-likeinterpretation

Structuredorfactualchecks

Improvementacrossmultipledrafts

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slide-018.jpg

OCR text:

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Scorer Tips

Use a higher-quality model for scoring, even if the prompt uses a cheaper

model. Scorers benefit from better reasoning and nuance.

Treat scorers like judges: evaluate intent match, style accuracy, and

overall output quality: -not just correctness.

Break scoring into multiple focused scorers (e.g., accuracy, Creativity,

formatting) to pinpoint issues.

Test scorer prompts in the Playground before use. Try strong and weak

outputs to refine scoring reliability.

Avoid overloading the scorer prompt with context. Focus if on the relevant

input and output for fair, consistent evaluation.

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BRAINTRUST.DEV

Playgrounds

Experiments

Evaluations

AIE

QuickiterationofPrompts,Agents,

Greatforcomparingfullexperimentssoyou

Scorers,Datasets

canreviewpastplaygroundsessions

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directly

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slide-023.jpg

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app.py

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code_conversion

push_prompts.py

prompts.create

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import braintrust

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from code_conversion.agents Iaport INSTRUCTLONS

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(“role”:"systee,“content:DNSTuCTINS),,

("role”:"user,"content”:"《(input))”).

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slide-024.jpg

OCR text:

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Tsresources.tsx

braintrust>Ts resources.ts

app

choicescores:

api

fair:0.5,

generate

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globals.css

ayout.tx

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page.tsx

braintrust

exportconstaccuracyScorerproject.

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name:"ChangelogAccuracyScorer",

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description: “Evaluates theaccuracy of a generatedcha

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You are evaluating the accuracy of a changelog gemerated from aList of git commits.

Tsutils.ts

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pubic

ProblemsOutputDebug ConsoleTerminalPorts

scripts

at callback(/Users/dpg/repos/ai-worlds-fair/node_modules/.pnpm/braintrusta0.0.205_openaia4.47.1_reacta18.3.1_sswr@2.2.

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env.localexample

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Finished running experiment

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