The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
Title: OpenAI Autodata - Agentic Synthetic Data & Evaluation Pipeline
Description:
Built an agentic pipeline for generating and evaluating synthetic data using multiple LLM roles.
The system uses challenger, weak solver, strong solver, and judge agents to create difficult examples, compare model behavior, and accept only examples that pass score-gap and validation checks.
The hardest part was making the evaluation deterministic enough to trust. I added fail-closed validation, auditable JSONL outputs, cost controls, and test coverage so bad or incomplete examples would be rejected instead of silently accepted.
Outcome:
Created a structured eval/data pipeline useful for building high-quality AI training and evaluation datasets.
Tech:
OpenAI, Python, LLM Evals, Synthetic Data, JSONL, Agentic Workflows👍
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Trending
retrofuturism
Share your favorite designs from the past exploring the distant future. Or your own take on the aesthetic 🚀
halftone
Dots. Texture. Grain. Let's see your designs using the halftone technique.
Midjourney
Creatives are showing off what they're making with Midjourney.
colorgrade
Video editors sound off: How do you enhance the mood of your video in color grading?
Related posts
A : hero created by Dither Engine -- product launch hero for an AI or computational modeling company.
B : hero created by Halftone Engine -- for business expense management, finance and AI SaaS
Which one do you like the most ?
👇 A or B ?
5 voted
56%
4 voted
44%
9 votes
Closed
The Descript Challenge is coming! 🎬🚀
Show us something from your creative workflow and teach the community how you do it.
Turn your process into something useful, inspiring, and worth sharing. 👀✨
Check out the guidelines and get started 👇 https://contra.com/community/topic/descriptchallenge/guidelines
Join now 👇 https://on.contra.com/kFk6FF
This has a premium feel.
Built a company intelligence and role-resolution system for TenderScope that connects fragmented company records across permits, tenders, contract awards and early construction signals.
The system normalizes company identities, matches records to canonical companies and verifies organizations against the OrgBook BC registry. Rather than simply identifying a company, it determines the role the company plays in a construction project — such as General Contractor, Developer, Architect, Construction Manager, Consultant or Subcontractor.
Each resolution is supported by confidence scoring and evidence, with safeguards that protect previously verified data and deadlock-safe PostgreSQL operations for reliable processing.
The result is a structured intelligence layer that turns inconsistent company mentions across multiple data sources into verified company identities, project relationships and evidence-backed construction roles.
