I'd like to share my experience with
AI Automation, LLM Optimization. When I began implementing these technologies in business processes, I was also interested in the issue of adapting models to context and training on relevant data in real time. In practice, I've noticed that a combination of methods provides the greatest efficiency: fine-tuning the model on a company's specific data, combined with the RAG approach, not only improves the accuracy of responses but also adapts the AI to constantly changing conditions. It's especially important to design the automation architecture so that scenarios can scale without losing quality. I use unit testing and regular data validation at each stage, which significantly reduces the number of errors. It's also important to continuously monitor model behavior and adjust its parameters as the workload increases.
