Summary

In this chapter, we explored LLMs and their potential to address real-world problems and create value across various applications. We discussed the key aspects of architecting LLM solutions, such as handling knowledge, interacting with real-time data and tools, evaluating LLM solutions, identifying and addressing challenges, and leveraging LLMs to build autonomous agents. We also emphasized the importance of retrieval-augmented language models for providing contextually relevant information and examined various techniques and libraries to improve LLM solutions.

We also discussed the limitations of LLMs, such as output and input limitations, knowledge and information-related challenges, accuracy and reliability issues, runtime performance challenges, ethical implications and societal impacts, and the overarching challenge of LLM solution adoption. To tackle these limitations, we presented various complementary strategies, such as real-time data integration, tool integration...