Enhancing Interaction with Large Language Models: A Catalog of Prompt Engineering Techniques
Bibliographic record
Abstract
As the utilization of large language models (LLMs) becomes increasingly prevalent, prompt engineering emerges as a vital skill for effective communication and output generation. Prompts serve as instructions that guide LLMs to enforce rules, automate processes, and achieve specific output qualities. This paper presents a comprehensive catalog of prompt engineering techniques framed as reusable patterns, analogous to software design patterns, aimed at addressing common challenges encountered in LLM interactions. Contributions to the field of prompt engineering are threefold: First, a structured framework is proposed for documenting and organizing prompt patterns, enabling adaptation across various domains and applications. Second, a detailed catalog of successfully implemented prompt patterns is provided to enhance LLM output quality and relevance. Finally, an approach is demonstrated for constructing prompts using multiple patterns, illustrating the synergistic effects that arise from combining different prompt techniques. By equipping researchers and practitioners with a systematic approach to prompt engineering, this paper aims to facilitate the effective application of LLMs in automating prompt development tasks and beyond.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".