10 Large language models in the real world
This chapter covers
- Recognizing errors, misinformation, and biases in LLM output
- Getting an LLM to say things its corporate overlords don’t want it to say
- Fine-tuning LLMs on your private data
- Vector search indexing for extractive and generative question answering
- Generating fact-based, well-formed text with LLMs
By increasing the number of parameters used in transformer-based language models to obscene sizes, you can achieve some surprisingly impressive results. Researchers call these surprises emergent properties, but they may be a mirage.1 Since the machine learning community started to become aware of the capabilities of really large transformers, they have increasingly been referred to as large language models (LLMs). The most sensational of these surprises is that chatbots built using LLMs can generate intelligent-sounding text. You’ve probably already spent some time using conversational LLMs, such as ChatGPT, You.com, and Llama. Perhaps, you hope that if you get good at prompting LLMs like these, they can help you get ahead in your career and even improve your personal life. Like most, you are probably relieved to finally have a search engine and virtual assistant that actually gives you direct, smart-sounding answers to your questions. This chapter will teach you how to use LLMs more efficiently, enabling you to advance beyond merely using them to sound intelligent.