Prompt Engineering Terms

Prompt engineering is the process of designing, refining, and optimizing inputs—known as prompts—to guide generative artificial intelligence (AI) systems toward producing specific, high-quality, and desired outputs. A prompt is typically a piece of natural language text that describes a task, such as a question, command, or instruction, and serves as the input to an AI model. The goal is to maximize the effectiveness, accuracy, and usefulness of the AI's response by carefully structuring the prompt.

This practice involves more than simply asking a question; it includes crafting instructions with precise language, specifying the desired format (e.g., JSON, a single sentence), providing context, and using techniques like step-by-step reasoning. For instance, using the "chain-of-thought" method, a prompt can be designed to induce a model to break down a complex problem into intermediate reasoning steps before arriving at a final answer. Other techniques include few-shot prompting (providing examples), zero-shot prompting (using phrases like "Let's think step by step"), and retrieval-augmented generation (RAG), which enriches the prompt with real-time information from external sources to reduce hallucinations.

Prompt engineering is considered a form of "programming" where the source code is natural language. It requires creativity, an understanding of the AI model’s capabilities and limitations, and iterative refinement based on trial and error. It is used across various applications, including text generation, image creation, code writing, summarization, and customer service chatbots. The process also involves maintaining a library of optimized prompts and continuously evaluating their performance to ensure relevance and effectiveness.

Keywords for prompt engineering

placeholders

mocks

“to do”

“for now”

simplified

Notes: AI systems usually prioritize rapid iteration over perfect first attempts, focusing on quickly refining and improving outcomes. This approach stems from the fundamental principle that multiple iterations, combined with consistent feedback, tend to produce significantly better results than attempting to solve complex problems in a single pass right from the start. As your project scales and grows, it is usually best practice to continually re-check the entire project ecosystem for any placeholder-type functions or components that require full and thorough iterations to ensure optimal performance and reliability.

Usage Example: “Check & show list of all placeholders(other keywords) in the project ecosystem”, “Replace all placeholders with complete integrations systematically through out the project ecosystem”

artificial limits

artificial elements

hardcoded artificial

Notes: AI usually implements artificial limits in coding to manage complexity and control behavior. This represents a complex interplay between technical constraints, safety requirements, and design philosophy. These carefully designed limitations are not arbitrary but serve critical functions in maintaining system reliability, preventing harmful or unexpected outputs, and ensuring practical usability under diverse conditions. While necessary and common during rapid prototyping phases, most artificial limits can eventually be removed and replaced with more natural, adaptive algorithmic coding approaches that allow for improved flexibility and stronger security constraints.

Usage Example: “Check for any hardcoded artificial limits in this directory that can be removed”, “remove all artificial elements in module 13244”

backward compatibility layers

legacy code

Notes: Project scaling with ai agents can be difficult due to many factors such as project size, code complexity, coding language, limited context window size of AI systems, etc. AI agents will naturally build enhancements, changes and optimizations through multiple architectural iterations, each building upon previous layers. The key insight is that AI systems implement multiple compatibility layers not as a design flaw, but as a strategic approach to managing complexity, enabling rapid iteration, versioning and ensuring long-term maintainability in evolving software ecosystems. Despite this fact multiple compatibility layers causes performance degradation, bloat, cognitive complexity, Maintenance Burden, Technical Debt Accumulation, Testing Complexity, Over-Engineering, Security Vulnerabilities, and variety technical issues that can be easily solved with little to no compatibility layers, especially when engineering from the ground up.

Usage Example: “remove all backwards compatibility layers in file 1111”, “move all backwards compatibility layers in directory fubar to directory dog”