Fine-tuning creates model-specific optimizations that quickly become obsolete. Blitzy favors developing sophisticated, system-level "memory" that captures enterprise-specific context and preferences. This approach is model-agnostic and more durable as base models improve, unlike fine-tuning which requires constant rework.

People struggle with AI prompts because the model lacks background on their goals and progress. The solution is 'Context Engineering': creating an environment where the AI continuously accumulates user-specific information, materials, and intent, reducing the need for constant prompt tweaking.

The current limitation of LLMs is their stateless nature; they reset with each new chat. The next major advancement will be models that can learn from interactions and accumulate skills over time, evolving from a static tool into a continuously improving digital colleague.

Adaption.AI is bucking the trend of building larger static models to focus on continual learning. Their core mission is to 'eliminate prompt engineering,' viewing it as a crutch that signifies a model's failure to truly adapt and learn from user interaction in real-time.

Building an AI application is becoming trivial and fast ("under 10 minutes"). The true differentiator and the most difficult part is embedding deep domain knowledge into the prompts. The AI needs to be taught *what* to look for, which requires human expertise in that specific field.

The early focus on crafting the perfect prompt is obsolete. Sophisticated AI interaction is now about 'context engineering': architecting the entire environment by providing models with the right tools, data, and retrieval mechanisms to guide their reasoning process effectively.

Unlike human employees who take expertise with them when they leave, a well-trained 'digital worker' retains institutional knowledge indefinitely. This creates a stable, ever-growing 'brain' for the company, protecting against knowledge gaps caused by employee turnover and simplifying future onboarding.

Treat AI skills not just as prompts, but as instruction manuals embodying deep domain expertise. An expert can 'download their brain' into a skill, providing the final 10-20% of nuance that generic AI outputs lack, leading to superior results.

AI has no memory between tasks. Effective users create a comprehensive "context library" about their business. Before each task, they "onboard" the AI by feeding it this library, giving it years of business knowledge in seconds to produce superior, context-aware results instead of generic outputs.