AI Post-Training Data

Post-training data is the information you use to refine an AI model after its initial training, and it matters because it shapes how the model behaves in real-world applications. Think of the base model as a chef who only tasted Italian food—ask it to make sushi, and you get marinara. The model is shaped by what it studied, so gaps in the data mean blind spots in its answers. Post-training fixes that by fine-tuning (adjusting the model on specific examples) for a specialty, like a general doctor picking cardiology.

You create post-training data by specifying what you want the model to do, often through environments (setups that define the scenario and interactions). These environments encapsulate the data you have, the tasks your agent will handle, and how it will interact. The goal is to improve the model based on the scenarios you actually see in production—your internal workflows and products. This loop revolves around refining the model with examples from your real use cases.

One workaround for limited data is synthetic data (AI-generated examples) or having the AI improve its own outputs through search, then training on those improved results. But if you naively train on AI-generated content, things can degrade fast. The hard part is figuring out which data to feed it—choosing the right examples to teach the desired behavior. That selection is what makes post-training data a strategic asset, not just a technical step.

AI post-training is how you take a general model and adapt it to your specific tasks using your own data. Start by gathering the raw material: logs, user feedback, or even instructions and prompts from your real workflows. That data is the fuel. You can even use AI to build high-quality training data automatically if you lack enough samples, since creating datasets from scratch is slow and costly.

The core idea is a learning loop, not a one-time fix. Feed your signals—such as past mistakes or new instructions—into the system, and let it replay. The goal is verifiable (testable) improvements, so you don't just trust the model got better. You run it in controlled environments (safe testing spaces) to check that each step works perfectly before moving on. For example, if step two fails, step six can't even happen, so you isolate and fix that part first.

To make this concrete, think of a recipe plus a chef. The recipe is a YAML file (a simple text config) defining repeatable, deterministic steps. The chef is the AI, like Claude or Codex, executing them. You stop rewriting the recipe each day; instead, you compose skills into workflows. Use AI to interview you, capturing the full context of a task—like recording a demo and pasting the transcript—so it can replicate what you do. Iterate until the AI handles 80% of the task, then keep refining using the traces (step-by-step logs) it produces to see what went wrong.

Fine-tuning (specializing a base model) means your AI is shaped by what you studied—if it only saw Italian food, it can’t make sushi well. Gaps in training data create blind spots (missing knowledge areas). To fix this, feed the model high-quality, targeted data that reflects your business rules. Undocumented rules in your head must be written down and fed to the AI, or it will repeat the same mistakes. Corrections need to be solidified (locked into future behavior) or they won’t stick.

Data is your moat (competitive edge)—generic AI doesn’t know your business until you train it. The difficult part is figuring out which data to feed. Use environments (settings for desired behavior) to specify what you want, encapsulating data and scenarios. Treat data and implementation holistically (as one system), not separate parts.

Verifiable outputs are critical. AI can be confident while completely wrong—always double check. Fix “real misses” (factually incorrect answers) by testing against ground truth (verified correct data). Flag missing information instead of guessing, and delete contradictory instructions so the AI follows one home. Auto-generated synthetic data (AI-created examples) helps when human data runs out, but check if that generated data is good enough—quality matters.