Prompt engineering is the process of crafting effective input prompts to guide LLMs in generating accurate and contextually relevant outputs. Since LLMs rely on patterns in the input text to produce responses, the way a prompt is structured can significantly impact the quality of the results. For example, asking “Summarize this document in three sentences” is more likely to yield concise outputs than simply stating “Summarize.”
Techniques in prompt engineering include specifying the format of the desired output, providing examples, and setting clear instructions. For instance, in a code generation task, a developer might use a prompt like “Write a Python function to calculate the Fibonacci sequence.” Providing examples within the prompt can also help, such as “Given input: 2, output: 4. Given input: 3, output: 9. What is the output for input: 5?”
Prompt engineering is especially important when fine-tuning is not an option, as it allows developers to extract task-specific results from a general-purpose model. By experimenting with phrasing, examples, and constraints, developers can optimize prompts to achieve the desired behavior efficiently.