Prompt Engineering

Prompt engineering is the iterative process of designing high-quality prompts that guide Large Language Models (LLMs) to produce accurate and desired outputs. While anyone can write a prompt, crafting the most effective one is complex, influenced by factors such as the model, its training data, configurations, word choice, style, tone, structure, and context. LLMs function as prediction engines, taking sequential text as input and predicting the next token based on their training data.

Effective prompt engineering involves optimizing various model configurations that control the LLM’s output, beyond just the prompt. Important settings include output length, which impacts computation, response times, and costs, and sampling controls such as Temperature, Top-K, and Top-P. Temperature controls the degree of randomness in token selection (lower for deterministic, higher for diverse results), with a setting of 0 being deterministic. Top-K selects the K most likely tokens, while Top-P selects tokens whose cumulative probability does not exceed a certain value. These settings interact, and extreme values can render others irrelevant. A common starting point for coherent, creative results is Temperature 0.2, Top-P 0.95, and Top-K 30.

Basic techniques include General/Zero-shot Prompting, which provides only a task description without examples. One-shot & Few-shot Prompting enhance model understanding by providing one or multiple examples of desired output structures or patterns. For few-shot classification tasks, it's essential to mix up the classes in examples to avoid overfitting. Additionally, System, Contextual, and Role Prompting are used to guide LLMs: System prompting sets the overall context and purpose, Contextual prompting provides specific, immediate task details, and Role prompting assigns a specific character or identity for the LLM to adopt.

More sophisticated techniques improve LLM capabilities for complex tasks: Step-back Prompting enhances performance by first prompting a general question, then using that answer as context for the specific task to activate relevant background knowledge. Chain of Thought (CoT) Prompting generates intermediate reasoning steps, leading to more accurate answers for complex tasks, and it generally requires setting the temperature to 0. Self-consistency combines sampling and majority voting to generate diverse reasoning paths and select the most consistent answer. Tree of Thoughts (ToT) generalizes CoT by allowing LLMs to explore multiple reasoning paths simultaneously. ReAct (Reason & Act) prompting enables LLMs to solve complex tasks by combining natural language reasoning with external tools (like search or code interpreters) in a thought-action loop.

Automatic Prompt Engineering (APE) is a method to automate the generation, evaluation, and refinement of prompts, alleviating the need for human input and enhancing model performance. LLMs also demonstrate strong Code Prompting capabilities across various programming languages, including writing code, explaining code, translating code from one language to another, and debugging and reviewing code by identifying errors, suggesting fixes, and offering improvements.

Key prompt design practices include providing examples (one-shot/few-shot) as a powerful teaching tool, designing with simplicity by using concise, clear language and strong action verbs, and being specific about the desired output to guide the model accurately. It is generally more effective to use positive instructions over constraints. Controlling the max token length in the prompt or configuration is crucial for managing response size and cost. Additionally, using variables in prompts makes them dynamic and reusable, especially for application integration, and experimenting with input formats and writing styles can yield different results.

For non-creative tasks, experimenting with structured output formats like JSON or XML is highly beneficial, as JSON helps ensure consistent style, focus on specific data, reduce hallucinations, and handle data types. Tools like json-repair can help fix truncated or malformed JSON output. Furthermore, working with JSON Schemas for input provides the LLM with a clear blueprint of expected data structure and types. Finally, a critical best practice is to document all prompt attempts comprehensively using a template (e.g., Name, Goal, Model, Temperature, Prompt, Output) to track performance across model versions, learn from results, and debug future errors, recognizing that prompt engineering is an iterative process of crafting, testing, analyzing, and refining.