# Optimizing Large Language Models: A Deep Dive into Effective Prompt Engineering Techniques
## 5 Simulation Result
### Overall Performance Analysis and PE Strategies
Thus, it is advantageous to assign appropriate weights to these new areas of knowledge and contexts, distill this knowledge, and integrate it into the generation process to enhance performance. Furthermore, it becomes evident that employing RAG with well-designed preprocessing based on PE strategies offers superior performance improvements compared to the mere application of raw R(B). Based on the above findings, the final PE strategies have been summarized as follows:  
1. Optimal Prompt Engineering strategies vary by dataset and model
* The effectiveness of a Prompt Engineering (PE) strategy depends on the characteristics of both the dataset and the LLM. * CoT and CoT-ICL combinations are generally effective for datasets requiring advanced reasoning (e.g., ARC, GSM8K, HellaSwag), while RAG-based techniques excel in datasets emphasizing factual accuracy (e.g., TruthfulQA). 2. Model-specific PE strategy preferences \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**PE Method** & **ARC** & **GSM8k** & **HellaSwag** & **MMLU** & **TruthfulQA** & **Winograde** & **Total Count** \\ \hline
**Category** & **Reasoning** & **Math** & **Reasoning** & **General** & **General** & **Reasoning** & **Total Count** \\ \hline
**C** & 11 & 8 & 10 & 11 & 3 & 13 & 56 \\ \hline
**T** & 6 & 8 & 8 & 4 & 14 & 7 & 47 \\ \hline
**I** & 6 & 9 & 5 & 3 & 3 & 9 & 35 \\ \hline
**S** & 8 & 4 & 2 & 3 & 13 & 5 & 35 \\ \hline
**R(C)** & 3 & 5 & 5 & 3 & 12 & 0 & 28 \\ \hline
**R(B)** & 1 & 0 & 0 & 1 & 0 & 1 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: The number of Prompt Engineering techniques used per dataset \begin{table}
\begin{tabular}{c c c c} \hline \hline
**LLM** & **Llama3** & **Gemma2** & **Mistral** \\ \hline
**C** & 18 & 14 & 25 \\ \hline
**T** & 17 & 12 & 16 \\ \hline
**I** & 9 & 6 & 20 \\ \hline
**S** & 10 & 14 & 11 \\ \hline
**R(C)** & 10 & 15 & 3 \\ \hline
**R(B)** & 1 & 0 & 2 \\ \hline Total Count & 65 & 61 & 77 \\ \hline \hline \end{tabular}
\end{table} 
Table 18: The number of Prompt Engineering techniques used per LLM. * Llama3: Performs best in tasks involving natural language understanding and reasoning (e.g., GSM8K, HellaSwag). * Gemma2: Excels in multiple-choice, science-related, and contextual reasoning tasks (e.g., ARC, MMLU). * Mistral: Demonstrates strength in datasets focused on truthfulness and error detection (e.g., TruthfulQA). * PE strategies amplify model strengths
* Advanced models (e.g., Gemma2) show enhanced performance with PE strategies, amplifying their inherent capabilities or mitigating weaknesses. * For example, Gemma2's superior performance on HellaSwag is further improved through CoT-based PE strategies. * CoT dominates as a universal strategy
* CoT was the most frequently used PE strategy across diverse datasets, highlighting its adaptability for reasoning and problem-solving tasks. * ToT, ICL, and SSR techniques also showed frequent usage but with varying effectiveness based on dataset requirements. * Performance of base PLM determines PE dependency
* Models with superior base PLM performance (e.g., Gemma2, Llama3) show reduced reliance on PE strategies. * Mistral, with lower base PLM performance, heavily depends on PE strategies to achieve competitive results. * RAG techniques and augmented preprocessing
* RAG methods excel when combined with CoT-based preprocessing rather than using raw data directly. * Enhanced performance is observed when PE strategies refine context or knowledge areas, particularly in fact-based datasets. * ICL dependency highlights model limitations
* Models with robust base PLM performance exhibit reduced dependence on ICL, suggesting adequate training during PLM development stages. * Mistral's higher reliance on ICL indicates potential gaps in its base PLM training. * Insights on general and specialized datasets
* General datasets benefit from aggregating common PE techniques, with CoT emerging as a universally effective strategy. * Specialized datasets require tailored approaches, with R(C) based techniques excelling in fact-based generation tasks.