Prompt Engineering for Applied Linguistics: Elements, Examples, Techniques, and Strategies
Bibliographic record
Abstract
Generative artificial intelligence, represented by large language models (LLMs), has the potential to revolutionize applied linguistics, such as language teaching, language learning, and language testing. However, how to write an effective prompt (i.e., prompt engineering) remains underexplored in applied linguistics. This study aims to elaborate on the important elements of prompt engineering, including persona, audience, contexts, instruction, and output specification. Examples were used to demonstrate how these elements can form an effective prompt in applied linguistics contexts. Besides, this study also delineates several important prompting strategies to handle more complex tasks, such as iterative prompting and few-shot prompting. Most importantly, it provides some practical tips to mitigate the potential shortcomings of LLMs, including data privacy, potential bias, explainability, and hallucinations, from the perspective of prompt engineering. This study highlights the potential applications of LLMs in applied linguistics by prompt engineering and methods in prompt engineering to navigate the potential pitfalls of LLMs, fostering the application of LLMs in applied linguistics effectively and responsibly.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".