Summary
Throughout this pivotal chapter, we have embarked on an in-depth exploration of the most recent and groundbreaking applications of LLMs, presented through comprehensive Python code examples. We began by unlocking advanced functionalities by using the RAG framework and LangChain, enhancing LLM performance for domain-specific tasks. The journey continued with advanced methods in chains for sophisticated formatting and processing, followed by the automation of information retrieval from diverse web sources. We also tackled the optimization of prompt engineering through prompt compression techniques, significantly reducing API costs. Finally, we ventured into the collaborative potential of LLMs by forming a team of models that work in concert to solve complex problems.
By mastering these topics, you have now acquired a robust set of skills, enabling you to harness the power of LLMs for a variety of applications. These newfound abilities not only prepare you to tackle current...
