CONCLUSION
BALANCING PERFORMANCE AND COST
IN THE EVOLVING landscape of GenAI, and particularly LLMs, a critical theme that emerges is the balance between performance and cost. This balance is not just a technical concern but also a strategic one for large and small enterprises, influencing how GenAI solutions are deployed and leveraged across various industries.
Throughout this book, we've explored diverse strategies and methodologies aimed at optimizing this balance. For instance, Chapter 2 delved into fine‐tuning techniques, highlighting how customizability can lead to more efficient use of computational resources. By tailoring models to specific tasks or domains, organizations can achieve better performance without proportionally increasing costs. This principle was also evident in the discussion of low‐rank approximations and parameter‐efficient fine‐tuning (PEFT) methods, which offer a more economical approach to model training and deployment.
Chapter 3 extended this discussion into the realm of inference techniques. Here, we examined how techniques such as prompt engineering and caching with vector stores can significantly enhance the efficiency of LLMs. By carefully crafting prompts and efficiently storing and retrieving vector data, we can reduce the computational load, thereby optimizing costs. Furthermore, the use of summarization and batch prompting, as outlined ...
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