The Language of AI: A Beginner's Guide to 12 Essential Concepts

I am very excited to say that I have joined Build With AI first Cohort program, to learn about AI and build tech products using AI.

After attending 1st Krishna Kumar session in Build School's first cohort Build with AI organized by Prashant Sharma . I got to know 12 important keywords (Which was new for me ??). Being from non tech background I had to study these 12 Keywords. I realized these are important 12 elements of language models. So understanding these are important to know the AI language systems.

So I spent sometime and tried to understand and make it simple for a non tech person. So I thought why not write an article about it.

So, here are the 12 essential components of AI language system:

1. Foundation Model: LLM designed to generate and understand human like text across a wide range of use-cases.

Think of this as a very smart computer program that can understand and write text like a human. It's versatile and can be used for many different tasks, like answering questions or writing stories. These models are the backbone of modern AI language systems. They're trained on vast amounts of text data, allowing them to understand and generate human-like text across various domains. Think of them as having a broad, general knowledge that can be applied to many different tasks. These models are the backbone of modern AI language systems. They're trained on vast amounts of text data, allowing them to understand and generate human-like text across various domains. Think of them as having a broad, general knowledge that can be applied to many different tasks.

Example: GPT (Generative Pre-trained Transformer) models, which can be used for various tasks like writing essays, answering questions, or even generating code.

2. Transformer: A popular LLM design known for its attention mechanism and parallel processing abilities.

This is a special way of building AI that helps it focus on important parts of text and process information quickly, like how we pay attention to key details in a conversation. This architecture revolutionized natural language processing. Its key innovation is the attention mechanism, which allows the model to focus on different parts of the input when producing each part of the output. This is crucial for understanding context in language.

Example: The BERT (Bidirectional Encoder Representations from Transformers) model, which is used in Google Search to better understand user queries.

3. Prompting: Providing carefully crafted inputs to an LLM to generate desired outputs.

This is like giving specific instructions to the AI to get the kind of answer or text you want. It's similar to how you might phrase a question to a person to get a helpful response. The art of prompting is becoming increasingly important. It's about finding the right way to "ask" the AI to perform a task. Good prompts can dramatically improve the quality and relevance of AI-generated content.

Example: Asking an AI, "Write a story about a magical forest in the style of Chetan Bhagat" to get a specific type of creative output.

4. RAG (Retrieval-Augmented Generation): Appending retrieved information to improve LLM response.

This method helps AI give better answers by looking up relevant information and including it in its response, kind of like how we might quickly check a fact before answering a question. This technique bridges the gap between static knowledge and dynamic information. It allows AI models to access and incorporate up-to-date information, making them more accurate and current in their responses.

Example: An AI assistant that, when asked about recent events, retrieves up-to-date information from a news database before formulating its response.

5. Knowledge Base (KB): Collection of documents from which relevant information is retrieved in RAG.

This is like a digital library that the AI can quickly search through to find useful information for answering questions or completing tasks. A well-structured knowledge base is crucial for RAG systems. It's not just about having information, but organizing it in a way that's easily accessible and updateable for the AI system.

Example: Wikipedia could serve as a knowledge base for an AI system to reference when answering general knowledge questions.

6. Vector Database: Stores vector representations of the KB, aiding the retrieval of relevant information in RAG.

This is a special way of organizing information that helps the AI quickly find what it needs, similar to how a library catalog helps you find books. These databases are optimized for the kind of searching that AI systems do. They store information in a format that allows for rapid, semantic-based retrieval, which is essential for quick and relevant AI responses.

Example: A system that converts book summaries into mathematical representations (vectors) for quick and efficient searching and recommendation.

7. Context-Length: Maximum number of input words/tokens an LLM can consider when generating an output.

This is how much information the AI can keep in mind at once when working on a task, like how much of a conversation you can remember when talking to someone. This is a key limitation in current AI systems. Longer context allows for more nuanced understanding and generation of text, but it also requires more computational resources. Balancing these factors is an ongoing challenge in AI development.

Example: An AI that can consider the last 2000 words of a conversation when formulating its next response.

8. Few-Shot Learning: Providing very few examples to an LLM to assist it in performing a specific task.

This is teaching the AI to do something new by showing it just a couple of examples, like how you might learn a new game by watching someone play it once or twice. This capability makes AI systems more flexible and easier to adapt to new tasks. It's particularly useful in scenarios where large amounts of training data aren't available.

Example: Showing an AI two or three examples of professional emails to help it learn how to write one, without extensive training.

9. Zero-Shot Learning: Providing only task instructions to the LLM relying solely on its pre-existing knowledge.

This is asking the AI to do something without any examples, relying on what it already knows, like asking someone to use their general knowledge to solve a new problem. This is perhaps the most impressive capability of advanced AI systems. It demonstrates true generalization ability, where the AI can perform tasks it was never explicitly trained on.

Example: Asking an AI to translate a sentence from English to Spanish without ever explicitly training it on translation tasks.

10. Fine-Tuning: Adapting an LLM to a specific task or domain by further training it on task-specific data.

This is like giving the AI extra practice on a specific type of task to make it better at that particular job, similar to how an athlete might focus on specific drills to improve certain skills. This process allows for the customization of AI models for specific applications. It's what enables the creation of specialized AI assistants for various industries and use cases.

Example: Taking a general-purpose language model and training it further on medical texts to create an AI assistant for doctors.

11. Instruction Tuning: Adjusting an LLM's behavior during fine-tuning by providing specific guidelines/directives.

This involves teaching the AI to follow specific rules or ways of doing things, like training a new employee on company policies and procedures. This is about shaping the AI's behavior and output style. It's crucial for ensuring that AI systems behave in ways that are safe, ethical, and aligned with human values.

Example: Training an AI to always include citations in its responses or to avoid using certain types of language.

12. Hallucination: Tendency of LLMs to sometimes generate incorrect or non-factual information.

This is when the AI makes mistakes or invents information that isn't true, similar to how people might sometimes misremember facts or fill in gaps in their knowledge with guesses. This is one of the biggest challenges in current AI systems. Understanding why hallucinations occur and developing methods to minimize them is a major focus of ongoing AI research.

Example: An AI confidently stating that "The Eiffel Tower was built in 1896" when it was actually completed in 1889.

This is what I learnt in just first session, I also learnt about Turing test, which is a test to determine a machine's ability to exhibit intelligent behavior indistinguishable from that of a human. If you interact with a machine and can’t tell whether it’s a human or a machine, then the machine has passed the Turing Test.

There is more to learn and will try my best to share my learnings.

I hope this guide has helped demystify some key AI concepts. As we've seen, AI is rapidly advancing. Stay tuned for more posts on how these technologies are shaping our world.