Generative AI (GenAI) is transforming how businesses innovate, automate, and solve problems. As this space evolves, many professionals are looking for a simple, accessible way to understand the key concepts and terminology. This reference guide serves as a foundational resource—designed to help readers navigate GenAI conversations with clarity and confidence.
In addition to defining core terms and tools, we’ve included links to related explainer blogs for those interested in a deeper dive. Whether just starting out or refining your strategy, this guide is built to support your journey through the expanding GenAI ecosystem.
AI Models | |||
Concept | Description | Why It's Important | Key Vendors |
AI Foundation Models | Large-scale models trained on extensive datasets to perform various tasks, such as generating text, images, and solving complex problems. | They power AI applications across industries, supporting personalization, automation, and scalability. | OpenAI (GPT-4, DALL-E 2), Google (Gemini, PaLM 2), Microsoft, Meta (Llama), NVIDIA, Anthropic (Claude) |
Domain-Specific Models | Models fine-tuned for industry-specific tasks in finance, healthcare, legal, and more, offering precision and compliance. | Help reduce risk, cut costs, and improve output accuracy for critical, regulated industries. | BloombergGPT (finance), Med-PaLM (healthcare), Salesforce Einstein (CRM), IBM WatsonX (enterprise AI) |
Reasoning AI Models | Designed to handle complex reasoning, these models interpret context, analyze logic, and provide insightful answers. | Support more precise decision-making and automation in complex, logic-driven environments. | OpenAI, Google DeepMind, Anthropic, IBM WatsonX, Microsoft Azure AI |
Multimodal AI | AI systems that combine multiple data types—text, image, audio, video—for richer and more accurate outputs. | Enable more context-aware, intelligent AI interactions and enhanced user experiences. | |
Agentic AI | Autonomous systems that plan, reason, and execute tasks independently, adjusting to evolving workflows. | Boost operational efficiency by enabling AI to take initiative, reducing manual oversight. | |
AI Agents | AI systems that interact with tools, APIs, and environments to autonomously perform and optimize tasks. | Increase agility by enabling scalable, responsive automation in dynamic environments. | OpenAI, Anthropic, Google DeepMind |
Conversational AI | Virtual agents use NLP to simulate human conversations, improving service response and automation. | Enhance customer engagement and scale service operations with lower response times. | |
Data Management | |||
Concept | Description | Why It's Important | Key Vendors |
Model Hubs | Repositories offering access to models, datasets, and APIs to accelerate AI integration and collaboration. | Promote faster adoption of AI with reusable resources and pre-trained assets. | Hugging Face, Replicate |
Vector Databases | Databases structured for storing and retrieving vector embeddings to support semantic search and matching. | Improve performance in AI-driven search and recommendation use cases. | Pinecone, Weaviate, Chroma |
Fine-Tuning Tools | Tools that customize foundation models using internal data for higher relevance and accuracy in output. | Tailor model behavior to business needs and reduce irrelevant results or hallucinations. | Scale AI, Snorkel AI, Cleanlab |
Governance & Security | |||
Concept | Description | Why It's Important | Key Vendors |
AI Trust, Risk, and Security Management (TRiSM) | Governance platforms that monitor and manage AI risks related to ethics, bias, privacy, and compliance. | Ensure AI systems operate ethically, securely, and in compliance with regulations. | Arize AI, Fiddler AI, Credo AI |
Tools | |||
Concept | Description | Why It's Important | Key Vendors |
Prompt Engineering Tools | Tools that help refine prompts to guide AI models toward better, more controlled responses. | Empower users to improve AI model output without requiring technical retraining. | Microsoft, PromptBase, LangChain |
GenAI Engineering Tools | Toolkits that help build, deploy, and manage AI workflows, ensuring security, compliance, and efficiency. | Simplify enterprise AI adoption with streamlined, governance-ready frameworks. | Microsoft Azure AI Studio, Databricks, MosaicML, Hugging Face |
Composite AI / Agent Frameworks | Use of multiple models/agents in workflows. | Handle complex tasks, reduce hallucination risks. | LangChain, Hugging Face Transformers Agent |
Deployment | |||
Concept | Description | Why It's Important | Key Vendors |
Model Deployment Tools | Platforms for deploying and managing models at scale, supporting automation and monitoring across environments. | Ensure cost-effective, monitored AI deployments across cloud and on-prem environments. | Anyscale (Ray), OctoML, Weights & Biases |
Next Steps in Your GenAI Journey
As GenAI continues to evolve, organizations must approach model selection, infrastructure planning, and governance with care. This guide provides a starting point for understanding the ecosystem and making informed decisions.
InterVision stays at the forefront of AI innovation—actively participating in industry discussions and helping customers design and implement strategies that balance opportunity with long-term value.
Let’s talk. Whether you’re exploring GenAI, modernizing your data, or planning your digital transformation, we’re here to support your next move. Reach out to start the conversation.
