Definition
Prompt engineering for large language models (LLMs) refers to the strategic design and refinement of input instructions to guide AI systems toward generating specific, accurate, and contextually relevant outputs.
This process combines linguistic precision with an understanding of model architecture to optimise interactions.
Effective prompt engineering accounts for 20-30% of output quality improvements in enterprise applications, making it essential for reliable AI deployment.
Key aspects of LLM prompt engineering include:
Structured instructions
Crafting prompts with explicit context, examples, or constraints to reduce ambiguity. For instance, specifying output formats (e.g., “Respond in bullet points”) or roles (e.g., “Act as a software engineer”) helps focus responses.
Iterative refinement
Using techniques like prompt chaining to progressively adjust outputs through follow-up queries, or automatic prompt engineering where secondary LLMs generate optimal prompts.
Context management
Balancing relevant background information against token limits, often employing strategies like code snippet inclusion or file referencing in developer tools.
Advanced techniques
- Chain-of-thought prompting: Breaking complex tasks into step-by-step reasoning sequences.
- Zero-shot learning: Eliciting desired behaviors without examples using phrases like “Let’s think step-by-step”.
The goal is to align the LLM’s vast training data with user intent, transforming a general-purpose model into a specialised tool for tasks ranging from code generation to creative writing.
