AI Models : a Primer

Created: July 2, 2024 | Last reviewed: September 2025 | Next review scheduled: March 2026

Why This Primer Matters

Artificial Intelligence (AI) is no longer a future trend—it is an everyday reality shaping how we work, create, and make decisions. Yet the field evolves so quickly that it can be difficult to separate fundamentals from passing hype.

This primer provides a timeless overview of AI, focusing on essential concepts, practical use cases, and strategies for applying AI responsibly. It is designed as a living resource—updated periodically to ensure relevance—so you can return here for a trusted baseline as the field advances.

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Jenn’s Take:
Over the past decade, technology trends have reshaped the job market. As storage expanded, costs dropped, and processing power accelerated, we unlocked the ability to work with massive datasets. First came the Big Data wave, followed by Machine Learning, and now the spotlight is firmly on Artificial Intelligence.

GPTs—Generative Pre-trained Transformers—are a type of large language model (LLM) built on neural networks and natural language processing. Trained on massive datasets, they can take an input and generate output—whether text, images, audio, video, or even 3D—at astonishing speed. Generative AI is both transformative and controversial: a productivity amplifier that enables rapid prototyping, lightning-fast content creation, and new ways to analyze and interact with information.

Today, GenAI tools are used to build chatbots, summarize long-form text, support students, draft blog posts, code websites, generate social content, analyze data, create and stylize images, animate visuals, build quizzes, plan travel, and more. Yet despite its speed, it isn’t perfect. Every stage—from crafting a good prompt to polishing a final draft—still requires a human touch.

I often think back to something I heard at AWE XR ’24: “AI is not generative, it is derivative.” AI doesn’t replace human creativity, context, or judgment—it enhances them. Its greatest strength lies in accelerating prototyping and helping us process information faster, while the truly creative breakthroughs remain uniquely human.

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What You’ll Learn

• Core concepts like LLMs and generative AI fundamentals
• Applications across sectors—software, marketing, customer support, and more
• Evergreen strategy: Selecting content formats (guides, tutorials, case studies) that age well
• Content optimization for AI-powered answer engines (AEO)
• Maintenance tips: How and when to update this primer
• Emerging trends—a future-proof glance at where AI content is heading

Essential Concepts

To understand modern AI, it helps to start with a few core ideas:

• Artificial Intelligence (AI): Broadly, the science of building systems that can perform tasks requiring human-like intelligence, such as perception, reasoning, and decision-making.
• Machine Learning (ML): A subset of AI where algorithms learn patterns from data rather than being explicitly programmed.
• Generative AI (GenAI): AI systems that create new content—text, images, code, or music—rather than only classifying or predicting.
• Large Language Models (LLMs): Deep learning models trained on massive text datasets, capable of generating human-like responses. Examples include GPT, Claude, Gemini, and LLaMA.
• Prompt Engineering: The craft of designing questions, instructions, or examples that guide an AI system toward useful outputs.
• Retrieval-Augmented Generation (RAG): A hybrid approach where AI combines live data retrieval with generative reasoning for accurate, context-aware answers.

How Generative AI Works

Training Data → Neural Networks → Transformer Architecture → Output Generation → Human Feedback (RLHF)

Generative AI models rely on a few important building blocks:

• Neural Networks: Layers of nodes that mimic the way neurons process signals, enabling models to learn complex relationships.
• Transformers: A breakthrough architecture that allows AI to understand relationships in sequences (like words in a sentence) with much greater efficiency.
• Training Data: Models are trained on vast datasets—billions of words, images, or code examples—that teach them probabilities of what comes next.
• Fine-Tuning: Models can be customized on specific datasets to specialize in areas like legal advice, healthcare insights, or software engineering.
• Reinforcement Learning from Human Feedback (RLHF): Human reviewers rate model outputs, teaching the AI to align better with human values.

Think of it this way: if traditional AI was like a calculator, generative AI is like a creative writing partner who has read the entire internet.

Core Use Cases Across Industries

AI is not just a single technology—it is a toolkit applied differently across domains:

💻 Software Development

• Code generation and autocomplete (e.g., GitHub Copilot).
• Debugging and test case generation.
• Automatic documentation and architecture diagramming.

🎧 Customer Support

• Virtual agents handling routine inquiries.
• Auto-summarization of support tickets.
• Knowledge base expansion with natural-language answers.

📈 Marketing & Content Creation

• Blog outlines, campaign ideation, and SEO optimization.
• Image and video generation for faster creative cycles.
• Personalized copywriting for emails and landing pages.

🏬 Supply Chain & Retail

• Demand forecasting with predictive models.
• Automated contract and invoice processing.
• Dynamic pricing and inventory optimization.

🏥 Healthcare & 💰 Finance (Emerging)

• Medical image analysis and diagnostic support.
• Risk modeling, fraud detection, and compliance automation.

Case Study Example:
A global retailer integrated generative AI into its internal IT helpdesk. Result: 35% reduction in ticket resolution time and measurable improvements in employee satisfaction.

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Glossary of Key Terms

• Generative AI (GenAI): Models that produce new content like text, images, or code.
• LLM (Large Language Model): A machine learning model trained on massive datasets of human language.
• RAG (Retrieval-Augmented Generation): A method of combining live data search with generative AI for more accurate answers.
• Token: The smallest chunk of text (word fragments) AI models process.
• Hallucination: When an AI confidently generates incorrect or fabricated information.
• Prompt Engineering: The practice of designing effective prompts to guide AI outputs.
• AEO (Answer Engine Optimization): Structuring content for conversational AI and answer engines rather than just traditional search engines.

Future Trends in AI

AI continues to expand in scope and capability:

• Multi-modal AI: Systems that combine text, images, audio, and video in a single workflow.
• Agents & Orchestration: AI moving from static tools to autonomous agents that plan, act, and collaborate.
• Ethics & Governance: New standards for responsible use, focusing on bias reduction, explainability, and compliance.
• Enterprise Integration: AI will increasingly be embedded into productivity suites, customer systems, and workplace collaboration tools.

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Further Resources & References

• Stanford AI Index Report
• OpenAI Learning Resources
• Answer Engine Optimization (AEO) Guide
• Evergreen Content Frameworks
• Responsible AI Principles

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GenAI Overview

Whether you are looking for a model or an application that leverages LLMs, it helps to understand the various tools available in the AI Tech Stack. Here’s a good source for finding some tools for your use case: TopAI Tools . I also hopped over to eWeek for news and updates and found a ranking of the top 150 AI tools.

Play with AI models

You can access most of the models below natively from their organization or in APIs, or you can use a number of cloud solutions providers such as AWS Bedrock, Google Cloud, Microsoft, etc.

• Kaggle for over 350k public data sets and over 1M public notebooks.
• Hugging Face