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.

3.1 Tailoring LLM Settings for Maximum Reliability

3.1.1 Optimizing Temperature for Predictable Outputs

3.2 Limit Output and Turns for Reduction of Hallucination Risk

3.3 Applying Frequency and Presence Penalties for Balanced Content

3.4 Minimizing Intrinsic Randomness for Stable Performance

3.5 Foundations of Prompt Engineering for Reliable LLMs

3.5.1 Designing Components of a Prompt for Reliability

3.5.2 Crafting Basic Prompts with a Focus on Dependability

3.6 Prompt Engineering Techniques for Preventing Hallucinations

3.6.1 Zero-shot prompting

3.6.2 Few-shot Prompting for Contextual Stability

3.6.3 Chain-of-thought Prompting for Transparent Reasoning

3.6.4 Automatic Chain of Thought (Auto-CoT) for Scalable Reasoning

3.6.5 Self-Consistency for Cross-Verification

3.6.7 Tree-of-Thought (ToT) Prompting for Structured Decision Making

3.7 Project: Creating a Reliable Weather Assistant with OpenAI's Function Calls

3.7.1 Building a Weather Assistant

3.7.2 Getting Started and Defining the Weather Function

3.7.3 Integrating the Chatbot

3.8 Summary

3.9 References