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Prompt Lab

A/B test prompts with eval metrics and caching.

4.8 (5.900) 5.9k booked v2.0 · updated recently
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Final price depends on your requirements, integrations and timeline.

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Tailored to your stack · NDA available
TypeScript
Prompt Lab
v2.0 · MIT

Overview

Prompt Lab is an essential experimentation toolkit for teams that treat prompt quality as a measurable product concern rather than trial-and-error guesswork. A prompt testing and evaluation environment where teams compare variants, measure outputs, track performance trends, and make better decisions about which prompt patterns should move into production. Useful for AI startups, feature teams, internal innovation groups, and consulting agencies.

Common use cases

Prompt A/B testingRegression detectionModel comparisonPre-rollout validationCost control

What's inside

  • Tailored to your stack
    Built around your chosen language, framework, providers and deployment target.
  • Production-ready patterns
    Streaming, retries, observability and guardrails baked in for real traffic.
  • Multi-provider ready
    Swap between OpenAI, Anthropic, Mistral, Azure or local models with one config.
  • Deployment recipes
    Drop-in guides for Vercel, Fly.io, Cloudflare Workers, Docker and Kubernetes.
  • Docs, tests & example app
    Comprehensive docs, integration tests and a reference app to learn from.
  • Priority implementation support
    Direct help from the team that built it during integration and rollout.

Why developers love it

Fast
Streaming responses and low-latency patterns out of the box.
Readable
Idiomatic, well-commented code your team can own.
Safe
Input validation, retries and cost guardrails included.
Product Preview · Website & Mobile App

See Prompt Lab in Action

A real admin console for your team and a native companion for your users — this is what a production deployment of Prompt Lab looks like day one.

🔒 scriptstore.app/prompt-lab/dashboard
◆ Prompt Lab
Experiments
Datasets
Evals
Cache
Reports
alex@studio
Experiment · summarizer-v4 vs v3
500 samplesgpt-4ocached 62%
Quality
8.9 8.2
▲ v4 wins
Latency
620ms 890ms
▲ v4 wins
Cost / 1k
$0.42 $0.51
▲ v4 wins
#
v3 output
v4 output
Judge
001
Long, meandering summary…
3 concise bullets w/ key figures
v4
002
Missed the CFO quote
Kept CFO quote and Q3 number
v4
003
Correct but flat tone
Structured, action-oriented
v4
004
Great, matches ref
Great, matches ref
tie
TypeScript · Web console
Dashboard — your team's control room.
9:41●●●●􀛨􀺶
Experiments
12 running · 4 shipped
summarizer-v4 vs v3
v4 ▲ 8%
classifier-multi-lang
tie
onboarding-tone
friendly ▲ 12%
extractor-json-strict
regression
Runs
Data
Reports
Me
Native mobile app
iOS + Android · tap-first companion.
More web screens

Every screen your team needs

🔒 scriptstore.app/prompt-lab/analytics
◆ Prompt Lab
Experiments
Analytics
Datasets
Evals
Cache
Reports
alex@studio
Prompt Lab · analytics
Last 30dBy experimentExport
Experiments
142
▲ 18
Win rate (new)
62%
▲ 8
Avg quality
8.9 / 10
▲ 0.4
Cache hit
62%
▲ 6
A/B win rate over time
24h7d30d
Recent experiments
summarizer-v4 vs v3v4 ▲ 8%
classifier-multi-langtie
onboarding-tonefriendly ▲ 12%
extractor-strict-jsonregression
Analytics
Product metrics for this script.
🔒 scriptstore.app/prompt-lab/knowledge
◆ Prompt Lab
Overview
Datasets
Judges
Prompts
API
Team
alex@studio
Prompt Lab · dataset library
SearchNew dataset
Categories
Summarization
Classification
Extraction
Tone
RAG
Tags
gpt-4osonnethaikugpt-4o-minigemini
summaries-500 · gold set
Dataset · Summarization · 500 rows · updated 1d ago
GoldPipelines
intents-multi-lang · 1200
Dataset · Classification · updated 2d ago
MultiPipelines
invoice-fields · extraction
Dataset · Extraction · updated 3d ago
ExtractPipelines
tone-friendly-vs-formal
Dataset · Tone · updated 4d ago
TonePipelines
rag-groundedness-judge
Judge · RAG · updated 5d ago
JudgePipelines
Knowledge base
Library of assets for this product.
More mobile screens

On-the-go companion

9:41●●●●􀛨􀺶
Runs today
18 running · 214 completed
summarizer-v4 · 500 samples
completed · v4 wins 68% · 3m ago
win
classifier-multi-lang · 1200
running 62% · now
run
extractor-strict-json · 300
regression -4% · 12m ago
reg
tone-friendly · 200 samples
friendly ▲ 12% · 22m ago
win
rag-groundedness · 400
completed · 96% grounded · 34m ago
ok
Runs
Datasets
Alerts
Me
activity
9:41●●●●􀛨􀺶
Search experiments
Prompt Lab
🔍 Search experiments, datasets, judges…
summarizerclassifierextractortonerag
summarizer-v4 vs v3
Summarization · relevance 96%
classifier-multi-lang
Classification · relevance 92%
extractor-strict-json
Extraction · relevance 88%
rag-groundedness-judge
RAG · relevance 84%
Home
Search
Alerts
Me
search
9:41●●●●􀛨􀺶
Account
Prompt Lab · settings
Devon Park
ML eng · Team plan
Regression alerts
Experiment complete
Weekly quality digest
Cache on
Eval runs this month
412 / 1,000
Runs
Datasets
Alerts
Me
profile