SPECIALISED AI MODELS: find what fits you!

Artificial Intelligence and its ever-evolving landscape have various specialised AI models for everyone to fit in. But to fit in, you must know which AI model will work best. Here in this article, we will look at some AI models and the specific tasks they are good at.

We will navigate you through the maze of this cluttered LLM’s multiverse. If you aren’t aware of LLM, NLP and other basics, here is the link!

What is Specialised AI?

Before diving into prompt engineering techniques, it’s essential to understand what we mean by “Specialised AI models.” Unlike general-purpose language models like GPT-3, which are trained on a broad range of internet text, specialised AI models are fine-tuned or specifically trained for particular domains or tasks. These could include:

1. Language Translations
2. Image Translations
3. Medical diagnosis models
4. Legal document analysis systems
5. Financial forecasting tools
6. Scientific research assistants
7. Industry-specific chatbots

These models have deep knowledge in their specific fields but may lack the breadth of general-purpose models. This specialisation necessitates a tailored approach to prompt engineering.

For ease of categorisation, we split the Specialised AI model into 3 primary groups:

1. Horizontal AI 
2. Vertical AI  
3. Common or generic AI

Horizontal AI Models 

Horizontal AI models are like all-rounders in the AI world. They can do a lot of different tasks across different industries because they’re designed to be versatile and adaptable. These models are usually built on general-purpose architectures and trained on large, diverse datasets. 

Let’s look at some examples: 

1. Natural Language Processing (NLP) models: These are great at understanding and generating human language. They can do cool stuff like machine translation, text summarisation, sentiment analysis, and question-answering. For instance, the popular GPT-3 model can write human-like text, create all kinds of creative content, and even translate languages. 

2. Computer Vision Models: These models specialise in understanding visual information. They can do things like figure out what’s in a picture, detect objects, and even create images. For example, a computer vision model could be used in a self-driving car to spot objects in its surroundings or create realistic images of people or animals. 

3. Recommender Systems: These are designed to suggest things that you might like based on what you’ve done before. You see them a lot in online shopping, streaming services, and social media. For example, a music streaming platform might use a recommendation system to suggest new songs or albums based on your listening history. 

Vertical AI Models 

Vertical AI models, also known as domain-specific AI, are like specialists. They’re tailored to do specific jobs in particular industries or fields. These models are often trained on more specialised datasets and are made to tackle the unique challenges and needs of a specific application. 

Here are some examples: 

1. Medical AI models: These are used in healthcare for things like diagnosing diseases, discovering new drugs, and personalised medicine. For example, a medical AI model could be used to analyse medical images to detect cancer or predict patient outcomes. 

2. Financial AI models: These are used in the financial industry for tasks like spotting fraud, assessing risks, and doing algorithmic trading. For instance, a financial AI model could be used to find fraudulent transactions or predict trends in the stock market. 

3. Industrial AI models: These are used in manufacturing and other industrial settings for tasks like predicting when machines might need maintenance, maintaining quality control, and optimizing processes. For example, an industrial AI model could be used to predict when a piece of machinery is likely to fail or to make production processes better. 

Common or Generic AI Models 

These are the all-purpose AI models that can be used for all sorts of things. They’re often used as the starting point for more specialised AI applications. 

Here are some examples: 

1. Neural Networks: These are like the brains of AI. They’re used in all kinds of AI applications, from recognising images to understanding human language. 

2. Deep Learning Models: These are a special type of machine learning that involves training deep neural networks with multiple layers. They’ve achieved top-notch results in areas like computer vision, natural language processing, and speech recognition. 

3. Reinforcement Learning Models: These models learn by interacting with an environment and receiving rewards or penalties. You see them in things like playing games, robotics, and self-driving cars.

How Horizontal, Vertical, and Generic AI Models Complement Each Other

While these three categories of AI models—horizontal, vertical, and generic—are distinct in their applications, they often work together to create more comprehensive AI solutions.

When it comes to AI, it’s all about how horizontal, vertical, and generic models can work together to address both general and specialised needs. For instance, picture a generic AI system handling simple queries within a broader AI setup, while a horizontal AI component manages interactions across different departments in a company. At the same time, a vertical AI model could tackle more specialised tasks within an industry, such as compliance or risk management in finance. 

By bringing these different AI models together, businesses and industries can build sophisticated systems that cater to a wide range of needs. It’s all about creating synergy and making sure that every aspect is covered, from general to specialised. 

Some Specialised AI models for specific tasks:

1. Sider AI: Sider Fusion/Sider is an advanced AI assistant that provides various functionalities such as chatting, writing, reading, translating, and image processing using AI technologies, including GPT-4o and Gemini. It integrates seamlessly with web pages and offers features like image-to-text, voice chats, and PDF interactions through its Chrome extension.
2. LibGen: Library Genesis, commonly known as LibGen, is a shadow library project that provides free access to millions of scholarly journal articles, academic and general-interest books, as well as images. It acts as an indexing system primarily used to facilitate file sharing, though it does not host any files itself.
3. Z-Library: Z-Library is a large online library that allows users to access a vast collection of ebooks, scholarly articles, academic texts, and general-interest books, making it a significant resource for free online reading and research.
4. Google Translate: One of my go-to app, with over 100+ languages, Google Translate is a free service by Google that instantly translates words, phrases, and web pages into a preferred language.
5. Sonix: Sonix is an automated transcription, translation, and subtitling platform that utilises advanced speech-to-text algorithms to convert audio and video files into text. It is designed for efficiency, processing audio faster than the recording duration, and offers high-level security to ensure the safety of transcripts and media files.
6. HiPdf is an AI-powered online tool developed by Wondershare that enhances PDF management by allowing users to chat with their PDFs and edit, convert, and manage documents. It provides an all-in-one solution for handling various PDF tasks, making it easier to summarise, explain, and rewrite content within PDFs.
7. NoteGPT: Do you also spend time searching for important points for vast, time-consuming PDFs? Not anymore! NoteGPT is a free online tool that saves you time and enhances your learning experience, summarises vast PDFs and converts PDFs into text.
8. MagicForm.AI: MagicForm.app is a platform that utilises AI to help users create quizzes quickly and easily. You can generate quizzes from various sources, such as URLs, PDFs, YouTube videos, or any topic you choose. The service is available for free, making it accessible to everyone looking to create interactive content.

Key Principles of Prompt Engineering for Specialised Models:

1. Domain-Specific Language: When working with specialised models, it’s crucial to use terminology and phrasing specific to the model’s domain. For instance, when prompting a medical AI, use proper medical terms rather than layman’s terms. This helps the model understand and respond more accurately.
2. Contextual Framing: Provide relevant context that aligns with the model’s specialisation. For a legal AI, you might frame your prompt within a specific area of law or jurisdiction.
3. Precision in Queries: Specialised models often require more precise queries than general-purpose models. Be specific about what you’re asking and what form you expect the answer to take.
4. Leveraging Model-Specific Features: Some specialised models may have unique features or capabilities. Your prompts should be designed to take advantage of these.
5. Ethical and Regulatory Considerations: Many specialised domains (like healthcare or finance) have strict ethical and regulatory guidelines. Ensure your prompts respect these boundaries.

Best Practices for Prompt Engineering in Specialised Domains:

1. Collaborate with Domain Experts: Work closely with experts in the specialised field to craft prompts that are both technically accurate and practically relevant.
2. Regular Evaluation and Refinement: Continuously assess the effectiveness of your prompts and refine them based on the quality of the AI’s outputs and feedback from end users.
3. Documentation and Standardisation: Develop a set of standardised prompts for common tasks within the specialised domain. Document these for consistency and ease of use.
4. Ethical Considerations: Develop guidelines for ethical prompt engineering specific to the domain. This might include avoiding prompts that could lead to harmful or discriminatory outputs.
5. User Education: If end-users will be interacting with the specialised AI, provide them with guidance on effective prompt crafting within the specific domain.
6. Feedback Loops: Implement systems to gather feedback on the AI’s responses. Use this feedback to improve both the model and your prompting strategies.
7. Cross-Domain Insights: While focusing on the specialised domain, don’t ignore insights from prompt engineering in other fields. Sometimes, techniques from one domain can be adapted to another.

It’s a complex but incredibly rewarding field that requires a deep understanding of both AI capabilities and the nuances of the specialised domain. By crafting thoughtful, precise, and context-rich prompts, you can unlock the full potential of these powerful tools. As AI technology continues to advance, the role of prompt engineering in specialised domains will only grow in importance. It serves as the crucial interface between human expertise and AI capabilities, enabling us to tackle complex, domain-specific challenges with unprecedented efficiency and insight. The key to success lies in continuous learning, adaptation, and a collaborative approach that brings together AI expertise and domain knowledge.

Remember, effective prompt engineering is as much an art as it is a science, requiring creativity, precision, and a deep understanding of both the AI’s capabilities and the specialised field it serves.

So, keep honing your skills in this area, and you’ll be making significant strides in leveraging the power of AI in specialised domains. Your expertise and dedication to prompt engineering will undoubtedly make a substantial impact.

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