IBM Patent Describes AI That Writes and Self-Grades Its Own Synthetic Data Code

By Patentlyze Team · Updated Aug 14, 2026

Building AI requires massive amounts of training data, and IBM just filed a patent for a system where one AI writes multiple versions of data-generating code and a second AI picks the best one automatically.

See all 4 drawings from this filing ↓

How IBM's two-AI pipeline produces synthetic data

Training AI models takes enormous amounts of data, and getting that data is expensive and slow. Companies increasingly use synthetic data (data that an AI makes up rather than collects from the real world) to fill the gap, but writing the programs that generate good synthetic data is itself a technical headache.

IBM's patent describes a two-step system. First, an AI is given a plain-English instruction and responds by writing several different code programs, each designed to produce synthetic data. Then a second, separate AI reviews all those code options and picks the one it judges to be the best. The winning code is run, and the synthetic data it produces is saved.

The appeal here is that the whole process runs without a human having to write or review any code. You describe what you need in plain language, and the pipeline handles the rest, from writing the programs to choosing between them.

How the evaluator AI picks the winning code version

The system works in three main stages:

• Prompt generation: A natural-language prompt (a plain-English instruction) is created that tells a generative AI model to produce multiple versions of workflow code (small programs whose job is to generate synthetic data). The AI writing the code has been specifically trained for code-related tasks.
• Code evaluation: All the generated code versions are handed to at least one evaluator AI model, also trained for code tasks, which compares them and selects the best one. The patent doesn't lock in a single selection method, leaving room for quality scoring, correctness checks, or other criteria.
• Data generation and storage: The selected code is actually executed, producing the synthetic data, which is then stored for later use in training other AI models.

The key architectural choice is the separation of generation (one AI writes options) from evaluation (a different AI judges them). This mirrors how software teams use code review: the person who wrote the code isn't the one who approves it. Both models are described as having been trained specifically on code-related tasks, which distinguishes them from general-purpose AI assistants.

What this means for AI training data at scale

For any company building AI models, high-quality training data is the bottleneck. Synthetic data is already a common workaround, but producing it well usually requires data engineers who know how to write the generation scripts and verify their output. A pipeline that automates both the writing and the selection step compresses that labor significantly.

Claim 1 of this patent is broad: it covers any method that uses a generative model to produce multiple code versions and an evaluator model to pick one, with the output being synthetic data. That breadth means IBM could assert this against a wide range of two-model code-and-judge pipelines, not just one narrow implementation. If granted, it would apply pressure on any AI platform that automates synthetic data generation this way. IBM's AI training data work sits among the plain-English patent summaries Patentlyze tracks across the synthetic data and enterprise AI space.

The drawings

4 drawing sheets from US 2026/0236235 A1 · click any drawing to enlarge

SHEET 1 SHEET 2 SHEET 3 SHEET 4

Browse all the drawings 4 drawing sheets

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