  Modern software systems are subjected to various types of uncertainties
arising from context, environment, etc. To this end, self-adaptation techniques
have been sought out as potential solutions. Although recent advances in
self-adaptation through the use of ML techniques have demonstrated promising
results, the capabilities are limited by constraints imposed by the ML
techniques, such as the need for training samples, the ability to generalize,
etc. Recent advancements in Generative AI (GenAI) open up new possibilities as
it is trained on massive amounts of data, potentially enabling the
interpretation of uncertainties and synthesis of adaptation strategies. In this
context, this paper presents a vision for using GenAI, particularly Large
Language Models (LLMs), to enhance the effectiveness and efficiency of
architectural adaptation. Drawing parallels with human operators, we propose
that LLMs can autonomously generate similar, context-sensitive adaptation
strategies through its advanced natural language processing capabilities. This
method allows software systems to understand their operational state and
implement adaptations that align with their architectural requirements and
environmental changes. By integrating LLMs into the self-adaptive system
architecture, we facilitate nuanced decision-making that mirrors human-like
adaptive reasoning. A case study with the SWIM exemplar system provides
promising results, indicating that LLMs can potentially handle different
adaptation scenarios. Our findings suggest that GenAI has significant potential
to improve software systems' dynamic adaptability and resilience.
