Summary

Conditioning allows steering generative AI to improve performance, safety, and quality. In this chapter, the focus is on conditioning through fine-tuning and prompting. In fine-tuning, the language model is trained on many examples of tasks formulated as natural language instructions, along with appropriate responses. This is often done through reinforcement learning with human feedback; however, other techniques have been developed that have been shown to produce competitive results with lower resource footprints. In the first recipe of this chapter, we implemented fine-tuning of a small open-source model for question answering.

There are many techniques for prompting that can improve the reliability of LLMs in complex reasoning tasks, including step-by-step prompting, alternate selection, inference prompts, problem decomposition, sampling multiple responses, and employing separate verifier models. These methods have been shown to enhance accuracy and consistency in reasoning...