Understanding Generative AI and Large Language Models

Looking for something to talk to your family about while you’re home for the holidays? Why not give them a clear, accessible explanation of ChatGPT? Here’s a simple, three-part framework that you can use to explain generative language models to (almost) anyone… TL;DR: We can explain ChatGPT pretty easily by focusing on three core ideas. 1. Transformer architecture: the neural network architecture used by LLMs. 2. Language model pretraining: the (initial) training process used by LLMs. 3. The alignment process: how we teach LLMs to behave to our liking. Although AI researchers might know these techniques well, it is important that we know how to explain them in simple terms as well! Why is this important? Generative AI has now become a popular topic among both researchers and the general public. Now more than ever before, it is important that researchers and engineers (i.e., those building the technology) develop an ability to communicate the nuances of their creations to others. A failure to communicate the technical aspects of AI in an understandable and accessible manner could lead to widespread public skepticism (e.g., research on nuclear energy went down a comparable path) or the enactment of overly-restrictive legislation. (1) Transformers: Most recent generative language models are based upon the transformer architecture. Although the transformer was originally proposed with two modules (i.e., an encoder and a decoder), generative LLMs use a decoder-only variant of this architecture. This architecture takes as input a sequence of tokens (i.e., words or subwords) that have been embedded into a corresponding vector representation and transforms them via masked self-attention and feed-forward transformations. (2) Pretraining: The most commonly-used objective for pretraining is next token prediction, also known as the standard language modeling objective. Interestingly, this objective—despite being quite simple to understand—is the core of all generative language models. To pretrain a generative language model, we curate a large corpus of raw text and iteratively perform the following steps: 1. Sample a sequence of raw text from the dataset. 2. Pass this textual sequence through the decoder-only transformer. 3. Train the model to accurately predict the next token at each position within the sequence. (3) Alignment: After pretraining, the LLM can accurately perform next token prediction, but its output is oftentimes repetitive and uninteresting. The alignment process teaches a language model how to generate text that aligns with the desires of a human user. To align a language model, we define a set of alignment criteria (e.g., helpful and harmless) and finetune the model (using SFT and RLHF) based on these criteria. For more details on how to conceptualize and explain generative language models, check out my recent overview: https://lnkd.in/g5eExZyj

Large Language Models (LLMs) are powerful, but their true potential is unlocked when we structure, augment, and orchestrate them effectively. Here’s a simple breakdown of how AI systems are evolving — from isolated predictors to intelligent, autonomous agents: 𝟭. 𝗟𝗟𝗠𝘀 (𝗣𝗿𝗼𝗺𝗽𝘁 → 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲) This is the foundational model interaction. You provide a prompt, and the model generates a response by predicting the next tokens. It’s useful but limited — no memory, no tools, no understanding of context beyond what you give it. 𝟮. 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) A major advancement. Instead of relying solely on what the model was trained on, RAG enables the system to retrieve relevant, up-to-date context from external sources (like vector databases) and then generate grounded, accurate responses. This approach powers most modern AI search engines and intelligent chat interfaces. 𝟯. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗟𝗟𝗠𝘀 (𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 + 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲) This marks a shift toward autonomy. Agentic systems don’t just respond — they reason, plan, retrieve, use tools, and take actions based on goals. They can:  • Call APIs and external tools  • Access and manage memory  • Use reasoning chains and feedback loops  • Make decisions about what steps to take next These systems are the foundation for the next generation of AI applications: autonomous assistants, copilots, multi-step planners, and decision-makers.

⭐️ Generative AI Fundamentals 🌟 In the Generative AI development process, understanding the distinctions between pre-training, fine-tuning, and RAG (Retrieval-Augmented Generation) is crucial for efficient resource allocation and achieving targeted results. Here’s a comparative analysis for a practical perspective: Pre-training:📚 • Purpose: To create a versatile base model with a broad grasp of language. • Resources & Cost: Resource-heavy, requiring thousands of GPUs and significant investment, often in millions. • Time & Data: Longest phase, utilizing extensive, diverse datasets. • Impact: Provides a robust foundation for various AI applications, essential for general language understanding. Fine-tuning:🎯 • Purpose: Customize the base model for specific tasks or domains. • Resources & Cost: More economical, utilizes fewer resources. • Time & Data: Quicker, focused on smaller, task-specific datasets. • Impact: Enhances model performance for particular applications, crucial for specialized tasks and efficiency in AI solutions. RAG:🔎 • Purpose: Augment the model’s responses with external, real-time data. • Resources & Cost: Depends on retrieval system complexity. • Time & Data: Varies based on integration and database size. • Impact: Offers enriched, contextually relevant responses, pivotal for tasks requiring up-to-date or specialized information. So what?💡 Understanding these distinctions helps in strategically deploying AI resources. While pre-training establishes a broad base, fine-tuning offers specificity. RAG introduces an additional layer of contextual relevance. The choice depends on your project’s goals: broad understanding, task-specific performance, or dynamic, data-enriched interaction. Effective AI development isn’t just about building models; it’s about choosing the right approach to meet your specific needs and constraints. Whether it’s cost efficiency, time-to-market, or the depth of knowledge integration, this understanding guides you to make informed decisions for impactful AI solutions. Save the snapshot below to have this comparative analysis at your fingertips for your next AI project.👇 #AI #machinelearning #llm #rag #genai