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Prompt Design and Engineering: Introduction and Advanced Methods
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Prompt design and engineering has rapidly become essential for maximizing the potential of large language models. In this paper, we introduce core concepts, advanced techniques like Chain-of-Thought and Reflection, and the principles behind building LLM-based agents. Finally, we provide a survey of tools for prompt engineers.
Forward citations
Cited by 9 Pith papers
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Large language models replicate and predict human cooperation across experiments in game theory
Llama-3.1-8B with a multi-step reasoning-and-filter prompt reproduces human cooperation rates across 121 dyadic games (MSD=0.031, r=0.89), outperforming Nash-equilibrium predictions (MSD=0.096, r=0.78).
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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes
ReFine combines rule-guided prompting and dual-granularity filtering to improve LLM-based tabular data generation when only 30 to 90 labeled rows exist, achieving top average rank over baselines.
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Ratas framework: A comprehensive genai-based approach to rubric-based marking of real-world textual exams
RATAS decomposes rubrics into simplified rules, scores each rule with GPT-4o, and cascades scores to grade long textual exam answers with reported near-human accuracy.
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LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents
On CUAD legal contracts, a prompt-engineered QWEN-2 pipeline with chunking and two answer-selection heuristics reportedly outperforms the fine-tuned DeBERTa-large baseline by about 9%, reaching claimed state-of-the-ar...
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Decoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking System
GEARS outperforms prompting baselines at selecting ranking policies from GAS-generated candidate sets, using tool-based filtering and feature-stability checks.
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AI Agent for Reverse-Engineering Legacy Finite-Difference Code and Translating to Devito
An AI agent combining GraphRAG, static Fortran analysis, and LLM code generation is reported to translate legacy Fortran finite-difference code into Devito, with Grade-A results claimed on roughly three-quarters of 13...
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An Evaluation of Large Language Models on Text Summarization Tasks Using Prompt Engineering Techniques
A broad benchmark of six open-weights LLMs shows prompt design and chunking affect summarization quality more than model size alone.
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Exploring Prompt Patterns in AI-Assisted Code Generation: Towards Faster and More Effective Developer-AI Collaboration
Using keyword matching on DevGPT conversations, the authors rank seven prompt patterns by a hand-weighted effectiveness score and claim 'Context and Instruction' and 'Recipe' reduce iterations.
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A Short Survey on Formalising Software Requirements using Large Language Models
A survey summarizing 35 papers on using LLMs to formalize software requirements, but it contains no new experimental results and its classification tables have errors.
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