Tuesday, November 14, 2023

g-f(2)1588 PQAM Unleashed: A Structured Approach to Amplifying AI Effectiveness


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Artificial intelligence, spearheaded by advanced language models like GPT-4 and Bard, has transcended its role as a mere technological curiosity to become a transformative force in the business world. These large language models (LLMs) and their generative foundational models (FMs) exhibit unparalleled capabilities, from image generation to human-like text creation, revolutionizing tasks across various industries. However, harnessing their full potential requires a nuanced understanding of the crucial role prompts and fine-tuning play in optimizing AI systems.

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genioux GK Nugget:

Effective utilization of AI hinges on the strategic deployment of prompts and fine-tuning. Clear instructions, encapsulated in well-crafted prompts, serve as the key to unlocking accurate and valuable outputs from AI systems.

genioux Foundational Fact:

To optimize the capabilities of generative AI, companies must invest in both prompts and fine-tuning. The sheer power of AI, trained on vast datasets, is unleashed through precise instructions. This necessitates a structured approach, as outlined in the prompt-query alignment model (PQAM), which acts as the guiding framework for creating prompts and fine-tuning AI models.

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  1. Power of Prompts and Fine Tuning:
    • Effective utilization of AI demands a balance between prompts and fine-tuning, ensuring optimal alignment with specific objectives.
  2. Role of Prompts:
    • Well-crafted prompts act as written instructions, guiding AI in generating accurate and valuable responses.
  3. Fine-Tuning for Task Specificity:
    • Fine-tuning allows customization of AI models for specific tasks, enhancing accuracy in domains like medical diagnosis and financial analysis.
  4. Quality Control through Fine-Tuning:
    • Fine-tuning facilitates quality control, crucial in industries where precision, such as healthcare and finance, is paramount.
  5. Personalization with Fine-Tuning:
    • Fine-tuning enables the personalization of AI responses, aligning with a brand’s tone, style, and values for a consistent customer experience.
  6. Efficiency through Fine-Tuning:
    • Fine-tuned AI models are more efficient, requiring fewer iterations and saving time and resources.
  7. Integrated Approach:
    • An integrated approach to prompts and fine-tuning offers great potential, ensuring a seamless and effective utilization of generative AI systems.
  8. Crafting Contextually Profound Prompts:
    • Executives are advised to employ prompt types such as query, clarification, comparison, and prediction to stimulate language model engagement and unearth unexpected insights.
  9. AI Reasoning Steps:
    • Fine-tuning is akin to AI reasoning steps, enhancing the system's insightfulness, accuracy, and transparency in generating responses.
  10. Human-in-the-Loop Emphasis:
    • While AI and PQAM enhance efficiency, human oversight remains critical, ensuring validation and ethical guidance.


As artificial intelligence continues to reshape industries, the synergy between human intelligence and machine capabilities emerges as the linchpin for unlocking its full potential. Crafting effective prompts and fine-tuning AI models, as facilitated by the internal PQAM framework, empowers executives to harness the transformative power of AI, fostering improved productivity, quality, and human upskilling. In this evolving age of AI, it is the harmonious integration of human ingenuity with machine capabilities that will propel us towards the most significant advancements.

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Colin W.P. Lewis and Agnieszka DziewulskaImprove Your Company’s Use of AI with a Structured Approach to Prompts, Harvard Business Review, November 14, 2023.

Colin W.P. Lewis 

Colin W.P. Lewis is a Professor of Artificial Intelligence and Data Science. He has built and applied for patents for several AI systems and works diligently to ensure safe and reliable AI is implemented. He previously worked at Palantir Technology and built and sold two software and audit system companies.

Agnieszka Dziewulska

Agnieszka Dziewulska is a PhD Candidate at University of Warsaw, faculty of Management focused on the materiality of the Social component of ESG. She is also Chief People and Culture Officer and heads the People function for KPMG Poland and Central and Eastern Europe, where she enables the people agenda and digital transformation. Agnieszka has been in HR leadership positions in global corporations for over twenty years with General Electric (GE), Hewlett Packard and IBM.

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