Entry Point closes the loop on fine-tuning, which is normally the point where the effort collapses. Preparing training data, formatting examples, running the job and deploying the result are four separate problems handled by four different tools, and most teams abandon fine-tuning at the data preparation stage.
Its approach is to let you transform raw data into training examples using a prompt, then generate the training set from that, then train and use the result without leaving the workflow. Data preparation being the bottleneck, making it a prompting task rather than an engineering task is the substantive contribution.
This is developer tooling for teams that have concluded prompting alone is not enough and need a model tuned on their own data. It is worth being clear that fine-tuning is not always the right answer; retrieval often solves the problem more at low cost, and the tool cannot make that judgement for you.
