Two and a half years ago, for my bachelor's final project, I had to use the easy LLMs through fine-tuning (aka “making the model do what specifics you have in your context instead of being too broad and general”). My project was to build a Voice translation English-Chinese demo app that uses a custom fine-tuned language model for language translation.

I noticed that there was a massive dataset of translated sentences (English to Chinese and Chinese to English) that I had to use for fine-tuning a Llama model (earlier version)…

The demo app worked great, and I graduated. Today, I just realized that this work can become outdated and almost irrelevant in a short amount of time, because:

1. Languages are evolving

2. New words are being inserted into daily life

3. Updates are being made, especially for the new generation

…this means, building a translation model or finetuning one without a proper system of self-improvement through an incremental structural context is not an excellent work.

Then comes the “Agentic Context Engineering” paper.

In short, if you’re thinking as an AI engineer, you’ll want to shift some focus from: “What model should I fine‐tune?” → “What context/playbook should my system maintain, evolve, and use?”.

Because if you’re relying on fine-tuning as your next solution for your product that needs adaptation over time:

• You’ll have to fine-tune again and again with new updates coming for a better context in your AI implementation

• It can get expensive

• It can get too technical for your team

…instead of using a “context playbook” that’s learning, being refined, adapting, and providing a fresh pattern for your AI within your system.

The ACE solution IS a problem solver even for many business cases.