Building LLM Powered Applications: Create intelligent apps and agents with large language models

• Embed LLMs into real-world applications
• Use LangChain to orchestrate LLMs and their components within applications
• Grasp basic and advanced techniques of prompt engineering

• Explore the core components of LLM architecture, including encoder-decoder blocks and embeddings
• Understand the unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLM
• Use AI orchestrators like LangChain, with Streamlit for the frontend
• Get familiar with LLM components such as memory, prompts, and tools
• Learn how to use non-parametric knowledge and vector databases
• Understand the implications of LFMs for AI research and industry applications
• Customize your LLMs with fine tuning
• Learn about the ethical implications of LLM-powered applications

Software engineers and data scientists who want hands-on guidance for applying LLMs to build applications. The book will also appeal to technical leaders, students, and researchers interested in applied LLM topics. We don’t assume previous experience with LLM specifically. But readers should have core ML/software engineering fundamentals to understand and apply the content.