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AI Glossary
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Prompt Engineering

Prompt engineering involves strategically designing and refining prompts to improve the performance and reliability of LLM outputs.

Short definition:

Prompt engineering is the strategic process of crafting, testing, and refining inputs to large language models (LLMs) to get more accurate, consistent, or useful results — often at scale and sometimes involving code or automation.

In Plain Terms

Prompt engineering takes prompt design to the next level.


It’s not just about writing one good input — it’s about:

  • Understanding how the AI “thinks”
  • Creating reusable templates
  • Embedding context and examples inside the prompt
  • Using tools or scripts to automate and optimize prompts for different use cases

It’s how developers and power users make AI models behave predictably and perform like custom-built tools.

Real-World Analogy

If prompt design is like writing one good instruction to an intern…
Prompt engineering is building a system of detailed checklists and examples so any intern could do the task, in your tone, with fewer mistakes — even as things change.

Why It Matters for Business

  • Improves reliability and output quality
    Great prompt engineering minimizes "AI weirdness" — giving you cleaner, safer, on-brand results.
  • Enables automation
    You can scale workflows like content generation, customer support, or reporting by running prompts through APIs or no-code tools.
  • Supports product features and internal tools
    Custom AI tools or assistants often rely on well-engineered prompts behind the scenes to behave correctly.

Real Use Case

An HR software company builds a hiring assistant that screens CVs and generates interview questions.


They use prompt engineering to:

  • Format responses consistently
  • Include context (e.g., job role, seniority)
  • Avoid bias
  • Integrate with their internal app through an API

This allows non-technical teams to use AI safely and reliably inside their platform.

Related Concepts

  • Prompt Design (The foundation — prompt engineering builds on it)
  • Few-Shot / Zero-Shot / Chain-of-Thought Prompting (Common prompting strategies)
  • Function Calling (Pairs with engineered prompts to tell LLMs when to call APIs)
  • RAG (Retrieval-Augmented Generation) (Sometimes paired with prompt engineering for more grounded responses)
  • LLMOps(Prompt engineering is often part of maintaining quality in production)