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ChatGPT Custom GPTs, Explained

ChatGPT Custom GPTs, Explained

ChatGPT

An explainer on ChatGPT's custom GPTs feature, what it actually does, and where building one genuinely saves time on recurring tasks.

Custom GPTs let anyone build a tailored version of ChatGPT for a specific, repeated task, and most people who could genuinely benefit from one have never actually built one.

A custom GPT is a version of ChatGPT configured with specific instructions, reference documents, and sometimes tool access, so you don’t repeat the same context every time you start a new conversation for a recurring task. It runs on the same underlying model, just with a saved, reusable configuration layered on top.

A task you do weekly with the same format every time, drafting a specific type of report. Work referencing the same documents repeatedly, upload them once rather than every conversation. Sharing a consistent workflow with a team, so everyone gets the same configured behavior instead of writing their own version.

Building one takes less setup than it sounds. Describe what you want it to do, upload reference documents, and ChatGPT handles most configuration through a conversational setup, no complex config files required.

If you find yourself repeating the same context in ChatGPT regularly, that’s the signal to build a custom GPT instead. See our full ChatGPT guide. See OpenAI’s own announcement for more detail.

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What Is Constitutional AI? Explained

What Is Constitutional AI? Explained

Claude

An explainer on Constitutional AI, the training approach behind Claude, and how it differs from purely human-feedback-based training.

Constitutional AI is the training approach behind Claude’s specific behavior, and understanding it explains a lot about why Claude responds to certain requests the way it does.

Rather than relying purely on human feedback to shape behavior, Constitutional AI trains a model against a written set of principles, a “constitution,” that it uses to critique and revise its own responses during training. The model learns to evaluate its own output against explicit written guidelines, not just implicit patterns in human ratings.

Training purely on human feedback has real limitations: it requires an enormous volume of human-labeled examples, and inconsistent guidance across many raters can make behavior harder to predict. A written constitution gives training an explicit, reviewable reference point instead.

When you notice Claude declining a request, adding a caveat, or explaining its reasoning for a boundary, that traces back to this training approach, not a simple keyword filter. The model is applying learned principles, not matching against a static blocklist.

Constitutional AI is a training methodology, not a fixed set of hardcoded rules, which is why edge-case behavior can involve genuine reasoning about competing considerations. See Anthropic’s own published research on Constitutional AI.