Part 5/5:
As developers lean more towards end-to-end architectures, the level of granular control over model outputs diminishes. Fine-tuning specific behaviors or correcting undesirable responses requires additional effort, often involving retraining or complex prompt engineering. This trade-off between simplicity and control is a fundamental issue in large-scale AI deployment.
The development and deployment of large language models remain a balancing act between cost, complexity, and control. As technology advances, it is likely that both costs will decrease and the ability to finely tune these models will improve, but the current landscape highlights the significant hurdles faced by organizations in this field.