Skip to content
← All posts

Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering

GPT-Lab · gpt-lab.eu · By Navneet Arora · September 17, 2026

AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.

Read on GPT-Lab → Opens the original article in a new tab.

More from the blog

GPT-Lab Sep 10, 2026

Responsible AI Must Consider Its Afterlife

AI may appear weightless, but every model depends on physical infrastructure. To understand responsible AI, we need to look beyond algorithms and consider the entire lifecycle of the hardware behind them. The post Responsible AI Must Consider Its Afterlife appeared first on GPT-Lab.

MIT News · Artificial Intelligence Sep 1, 2026

Ila Kumar: Innovating with communities

The PhD student works to give young people an active role in shaping digital technologies that can support their own well-being.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

GPT-Lab Aug 19, 2026

Zero Human Organization

What happens when AI moves beyond automating individual tasks and starts operating an organization? GPT-Lab’s Zero Human Organization experiment explores what becomes possible when AI takes on the day-to-day operational loop. The post Zero Human Organization appeared first on GPT-Lab.

Related papers

#computer vision Review Apr 2024

AI-powered Code Review with LLMs: Early Results

The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.

Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al. · 62 citations · ⚡3
#computer vision Feb 2024

Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis

The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.

Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al. · 41 citations
#computer vision Apr 2024

Large Language Model Evaluation Via Multi AI Agents: Preliminary results

A novel multi-agent AI model is introduced that aims to assess and compare the performance of various LLMs, and initial results indicate that the GPT-3.5 Turbo model's performance is comparatively better than the other models.

Z. Rasheed, Muhammad Waseem, Kari Systä et al. · 23 citations
#artificial intelligence Book Open access Apr 2020

A Multiple Case Study of Artificial Intelligent System Development in Industry

This investigation revealed different types of AI systems and different AI development approaches, but it is common that business opportunities involving with AI systems are not validated and there is lack of business-driven metrics that guide the development ofAI systems.

Anh Nguyen-Duc, Ingrid Sundbø, E. Nascimento et al. · 20 citations · ⚡1

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.