# Prompt engineering and framework: implementation to increase code reliability based guideline for LLMs

arXiv: https://arxiv.org/abs/2506.10989 · HTML: https://mlsanity.com/paper/2506.10989/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 26 references that are in the map.
- First posted on arXiv 19 Mar 2025.

## Builds on (most cited first, 22 of 22)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [What Makes Good In-Context Examples for GPT-3?](https://mlsanity.com/paper/2101.06804/) (2021)
- [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://mlsanity.com/paper/2104.08786/) (2021)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [InCoder: A Generative Model for Code Infilling and Synthesis](https://mlsanity.com/paper/2204.05999/) (2022)
- [PTR: Prompt Tuning with Rules for Text Classification](https://mlsanity.com/paper/2105.11259/) (2021)
- [Granite Code Models: A Family of Open Foundation Models for Code Intelligence](https://mlsanity.com/paper/2405.04324/) (2024)
- [Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering](https://mlsanity.com/paper/2401.08500/) (2024)
- [Fairness-guided Few-shot Prompting for Large Language Models](https://mlsanity.com/paper/2303.13217/) (2023)
- [WorkflowLLM: Enhancing Workflow Orchestration Capability of Large Language Models](https://mlsanity.com/paper/2411.05451/) (2024)
- [PromSec: Prompt Optimization for Secure Generation of Functional Source Code with Large Language Models (LLMs)](https://mlsanity.com/paper/2409.12699/) (2024)
- [FRANC: A Lightweight Framework for High-Quality Code Generation](https://mlsanity.com/paper/2307.08220/) (2023)
- [DocCGen: Document-based Controlled Code Generation](https://mlsanity.com/paper/2406.11925/) (2024)
- [Automated Prompt Engineering for Cost-Effective Code Generation Using Evolutionary Algorithm](https://mlsanity.com/paper/2408.11198/) (2024)
- [Beyond Code Generation: Assessing Code LLM Maturity with Postconditions](https://mlsanity.com/paper/2407.14118/) (2024)

## Built on by (most cited first, 1 of 1)

- [Suffix-Constrained Greedy Search Algorithms for Causal Language Models](https://mlsanity.com/paper/2603.01243/) (2026)

## Abstract (arXiv)

In this paper, we propose a novel prompting approach aimed at enhancing the ability of Large Language Models (LLMs) to generate accurate Python code. Specifically, we introduce a prompt template designed to improve the quality and correctness of generated code snippets, enabling them to pass tests and produce reliable results. Through experiments conducted on two state-of-the-art LLMs using the HumanEval dataset, we demonstrate that our approach outperforms widely studied zero-shot and Chain-of-Thought (CoT) methods in terms of the Pass@k metric. Furthermore, our method achieves these improvements with significantly reduced token usage compared to the CoT approach, making it both effective and resource-efficient, thereby lowering the computational demands and improving the eco-footprint of LLM capabilities. These findings highlight the potential of tailored prompting strategies to optimize code generation performance, paving the way for broader applications in AI-driven programming tasks.