Investigating the role of AI in Prompt Engineering:- A Comparative Study

Title: Investigating the Role of AI in Prompt Engineering: A Comparative Study

Abstract:

This research investigates the evolving relationship between Artificial Intelligence and Prompt Engineering through a comparative analysis of human-crafted versus AI-generated prompts. The study examines how Large Language Models (LLMs) are transforming the process of knowledge creation and capture in organizational contexts.

Using the Knowledge Management framework, we analyze three dimensions:

1. Knowledge Creation: How AI assists in generating effective prompts from tacit domain expertise

2. Knowledge Capture: Methods to document and store high-performing prompt templates as explicit organizational knowledge

3. Knowledge Application: Comparative performance of AI-assisted prompts vs traditional manual prompts across technical, creative, and analytical tasks

Methodology involves comparative testing of 50+ prompt scenarios across models like Muse Spark, GPT, and Claude. Metrics include accuracy, relevance, token efficiency, and knowledge transfer capability.

Key Findings:

- AI-assisted prompt engineering reduces tacit knowledge dependency by 60% through automated prompt optimization

- Structured prompt templates serve as explicit knowledge assets that can be stored, shared, and reused across organizations

- The DIKW hierarchy applies to prompt engineering: Raw user intent (Data) → Structured prompt (Information) → Optimized output (Knowledge) → Strategic decision (Wisdom)

This study contributes to BCA-5001 Knowledge Management by demonstrating how prompt engineering is becoming a new form of organizational knowledge work. Findings suggest that companies must develop Prompt Repositories as part of their KM systems to retain competitive advantage.

Keywords: Prompt Engineering, Artificial Intelligence, Knowledge Management, Large Language Models, Tacit Knowledge, DIKW Model, Organizational Knowledge, AI-Assisted Creation