AI & YOU #8: Secrets to Creating AI Experts, ChatGPT Persona Building

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Imagine having an AI assistant that’s an expert in any domain you need, all within seconds. Whether you need a social media manager, blog writer, SEO specialist, or even something like a financial advisor, you can create AI versions of these roles with ChatGPT personas. Think of it like a chat bot that you created to fill a tailored role just for you, or your team all by crafting the right initial prompt. We share our experience below.

In this week’s edition of AI & YOU, we are diving into ‘personas’ in prompt engineering, looking at how they are key to contextual and efficient ChatGPT interactions. We also highlight the biggest reason Google Bard falls behind ChatGPT. (Spoiler: it’s the former’s lack of ‘imagination’)

If your enterprise needs help leveraging ChatGPT by developing industry- or task-specific expert personas, get in touch! 

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AI & YOU #8: Secrets to Creating AI Experts, ChatGPT Persona Building

June 18, 2023

OpenAI’s ChatGPT is a shining example of how artificial intelligence is being increasingly integrated into our lives. This powerful language model has the capability to emulate human-like text, be it answering questions, writing essays, summarizing documents, or even creating poetry. But its ability to adapt to different styles and contexts is where the concept of personas truly comes into its own.

Understanding Personas in ChatGPT

A ‘persona’ in ChatGPT is akin to a character profile, encompassing attributes like background, personality, skillset, likes, and dislikes. Crafting a persona shapes the behavior and responses of the AI model. A well-defined persona helps in creating a more relatable, engaging, personalized, and expert user experience.

The importance of personas in ChatGPT extends beyond just personalization. A well-defined persona can bring about computational efficiency. By assigning specific attributes to the model, you can guide its responses more effectively, leading to less resource usage.

Effective Strategies for Persona Creation

Creating a successful persona for ChatGPT involves two main components: defining the persona’s traits and implementing them through effective prompt design. In essence, persona creation in ChatGPT is a kind of ‘persona-driven engineering solution’.

One common method for implementing traits in prompt design is through the ‘system message’. A system message is a type of prompt that sets the context for ChatGPT’s responses. For example, if we have a persona that’s an expert in 20th-century literature, our system message could be, “You are an AI model with extensive knowledge of 20th-century literature.”

Adding Context to Prompts and Responses

When creating a persona for ChatGPT, it’s not just about establishing the traits; it’s also about adding context. Context helps ChatGPT better understand the nature of the conversation and respond accordingly.

Optimizing Personas for Computational Efficiency

One key benefit of creating personas for ChatGPT is the impact on computational efficiency. The persona system helps reduce the tokens required to establish context, which is a boon when we consider the constraints of the OpenAI API – specifically, the token limit for each interaction. By using personas and context effectively, we can reduce token usage while maintaining the richness of the interaction.

Embracing the Power of Personas

Personas form an integral but largely untapped part of powerful AI models like ChatGPT. By introducing specific personality traits, skillsets, likes, and dislikes, we enable the AI to deliver expert, context-aware interactions that meet user needs more effectively.

Why Google Bard is Inferior to Open AI’s ChatGPT

AI is an ever-evolving field, with tech giants like Google and OpenAI leading the charge in innovation. Both have developed state-of-the-art language models – Google’s Bard and OpenAI’s ChatGPT – that are capable of generating human-like text. While both are groundbreaking in their own ways, there is a key factor that sets them apart: the power of imagination.

In this context, imagination refers to the ability of a language model to adapt its responses based on specific characters, or ‘personas’, and to be fine-tuned for different tasks or domains. While both models possess remarkable language generation capabilities, it is ChatGPT’s ability to imagine – to take on personas and fine-tune its responses – that gives it the upper hand over Google’s Bard.

ChatGPT’s Fine-Tuning Flexibility and Bard’s Lack of Imagination

ChatGPT exhibits a remarkable level of flexibility in fine-tuning. It can be meticulously fine-tuned for various tasks including language translation, summarization, text completion, and persona embodiment. This means that ChatGPT can be calibrated to “become” a specific persona, assuming traits, skillsets, and backgrounds that enhance the AI’s interaction with users.

Contrastingly, Google Bard, despite being a robust AI model, has significant limitations in terms of fine-tuning and persona creation. Bard is specifically designed for sequence-to-sequence tasks, such as language translation, text summarization, and text generation. Unlike ChatGPT, Bard cannot assume personas or be fine-tuned beyond these initial tasks.

Why Imagination Matters in AI

In a world increasingly reliant on artificial intelligence, the ability of an AI model to “imagine,” to embody a persona, and to fine-tune its responses is not just an interesting feature—it’s a game changer.

This imaginative capacity, embodied by OpenAI’s ChatGPT, allows for more engaging, personalized, and expert-level interactions, significantly enhancing the user experience. Through careful fine-tuning and the strategic implementation of personas, ChatGPT transcends the limitations of a conventional AI language model, evolving into a tool that can cater to diverse requirements across various domains.

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*Skim AI is a Machine Learning and Artificial Intelligence consultancy that educates executives, performs due-diligence, advises, architects, builds, deploys, maintains, updates and upgrades enterprise AI across language (NLP), vision (CV) and automation based solutions.

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