2 Generating trustworthy responses with prompt engineering

Prompting has become an important technique for using the capabilities of large language models (LLMs) effectively. 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 such as GPT, Claude, and Gemini.

2.1 Choosing the right model

2.1.1 Reasoning vs. nonreasoning models

2.1.2 The cost-capability spectrum

2.1.3 Model selection as a reliability decision

2.2 Tailoring LLM settings for maximum reliability

2.2.1 Optimizing temperature for predictable outputs

2.2.2 Top-P Sampling technique

2.3 Limiting output and turns to reduce hallucination risk

2.4 Applying frequency and presence penalties for balanced content

2.5 Minimizing intrinsic randomness for stable performance

2.6 Foundations of prompt engineering for reliable LLMs

2.6.1 Designing components of a prompt for reliability

2.6.2 Crafting basic prompts with a focus on dependability

2.7 Prompt engineering techniques for preventing hallucinations

2.7.1 Zero-shot prompting

2.7.2 Few-shot prompting for contextual stability

2.7.3 Chain-of-thought prompting for transparent reasoning

2.7.4 Enhanced CoT prompt

2.7.5 Automatic CoT for scalable reasoning

2.7.6 Self-consistency for cross-verification

2.7.7 Tree-of-thought prompting for structured decision-making

2.8 Project: Creating a reliable weather assistant with OpenAI’s function calls