In the evolving landscape of technology, Generative AI (GenAI) and Large Language Models (LLMs) have emerged as critical areas of knowledge for software architects. A recent survey revealed that 90% of software architects believe that understanding GenAI is essential for their role. This shift comes as GenAI introduces new paradigms that challenge traditional software design principles. Unlike deterministic algorithms, GenAI solutions are non-deterministic, requiring architects to think in terms of success percentages rather than absolute correctness. The development effort in GenAI does not scale linearly with results; some problems can be solved with simple prompts, while others demand complex workflows or resource-intensive fine-tuning. Software architects must now assess tradeoffs between using GenAI and traditional methods, deciding when to apply techniques like fine-tuning or Retrieval-Augmented Generation (RAG). This knowledge is crucial for making informed architectural decisions in modern systems, ensuring that architects can lead effectively in this new era of technology.
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