3 Minimizing hallucinations and enhancing reliability with prompt engineering techniques
This chapter covers
- Tailoring the settings of LLMs for maximum reliability
- The foundations of prompt engineering for reliable LLMs
- Prompt engineering techniques to reduce hallucinations.
Prompting has become an important technique for effectively utilizing the capabilities of large language models (LLMs). Carefully designed prompts provide the context, instructions, and examples needed to guide LLM text generation for a variety of applications. Prompt engineering involves the iterative process of constructing, analyzing, and refining prompts to produce high-quality outputs from models like GPT-4 and Claude.
Well-formulated prompts serve as targeted programs that steer the model towards the desired behavior. With experimentation, one can develop expertise in prompt engineering to tap into diverse LLM capabilities in summarization, translation, reasoning, and creative generation. Prompt optimization leveraging reinforcement learning is an active area of research.
Prompt engineering skills allow better understanding of LLM strengths and limitations. Prompting enables augmenting LLM knowledge with external data and tools. Furthermore, effective prompting is key to improving LLM safety and aligning output to human preferences.