Building Smarter Systems – Advanced LLM Integrations

LLMs, such as GPT-4, have exploded in capability. But simply using their basic text completion APIs only scratches the surface of their potential. Going beyond isolated generation allows LLMs to reason, research, converse, and take action.

Integrating LLMs into our existing tools and workflows is crucial for transforming these models from novelties into productivity powerhouses. In this chapter, we’ll explore advanced techniques to level up how you apply LLMs by connecting them to your tech stack.

This chapter provides a general overview of these topics. Covering them in deeper detail could require one or more books just on some of these topics.

The following topics will be covered in this chapter:

• Automating bulk prompting at scale using platforms such as Google Sheets, Zapier, and Make
• Building custom AI pipelines with developer tools such as LangChain, Flowise, and Langflow
• Leveraging diverse models’ specialized strengths by chaining multiple LLMs
• Walk-throughs of sample integrations for competitive intelligence, customer data, and document analysis
• Emerging innovations such as multimodal models and LLM app plugins

Overall, you’ll learn about approaches to unlock LLMs’ full potential as reasoning assistants that can search, converse, create, and take action. Integration grants LLMs context and connectivity to enhance real applications rather than just produce text in isolation.

Let’s start by exploring an accessible option for automating bulk prompting across many records – spreadsheet templates and integrations.