Generative AI
Generative AI is changing how software creates, understands and works with information. Understand the technology, the architecture around it and what it takes to make AI useful in production.
Explore Generative AIGenerative AI
Understand how generative AI works, where it fits in modern digital systems and what matters when moving from experimentation to production.
What is Generative AI?
Generative AI refers to AI systems that learn patterns from data and use those learned representations to generate new content such as text, code, images, audio and other media. Its practical value depends not only on the model, but also on the data, context, retrieval, software and operational systems surrounding it.
Why Generative AI matters
Generative AI matters because software can increasingly work with information in ways that were previously difficult to automate. It can summarize, transform, explain, generate and interact with knowledge while remaining connected to existing digital workflows.
Knowledge work
AI can help people search, summarize, transform and reason across large volumes of information.
Software development
Generative models can assist with code generation, explanation, testing, documentation and developer workflows.
Digital experiences
Conversational and multimodal systems can create more adaptive ways for people to interact with products and information.
Business automation
Generative AI can become part of workflows that classify, draft, extract, transform and route information.
The building blocks
Modern generative AI systems are built from several connected capabilities rather than a single model.
Foundation models
Large models learn broad representations from extensive datasets and can be adapted to many downstream tasks.
Language models
Language models generate and transform text by predicting sequences from learned patterns and context.
Multimodal AI
Modern systems can work across combinations of text, images, audio, video and other information types.
Embeddings
Embeddings represent information in numerical form so systems can compare meaning and retrieve related information.
Retrieval
Retrieval systems connect models with relevant external information rather than relying only on model parameters.
Agents
Agentic patterns combine models with tools, context, planning and control loops to perform multi-step tasks.
From model to system
A useful generative AI application is a system, not simply a model API. Production architecture has to connect intelligence with trustworthy information, application logic and operational controls.
Data and context
Models need access to the right information and context for the task they are expected to perform.
Retrieval and grounding
Retrieval can provide current or organization-specific information and reduce dependence on model memory alone.
Orchestration
Applications often need software logic that determines which models, tools, retrieval systems and workflows are used.
Evaluation
AI behavior needs explicit evaluation because correctness, relevance, safety and usefulness cannot be assumed from a successful API call.
Engineering considerations
Moving generative AI into production introduces engineering concerns that are different from conventional application development. Teams need to understand model behavior, evaluate system quality and design for security, reliability and operational visibility.
Quality and evaluation
Define measurable expectations for accuracy, relevance, consistency, latency and user value.
Security
Protect sensitive information, control model and tool access, and consider prompt injection and unsafe outputs.
Observability
Track requests, retrieval behavior, model usage, latency, failures and other signals needed to understand system behavior.
Cost and latency
Model selection, context size, retrieval design and workload patterns influence both user experience and operating cost.
Governance
Responsible deployment requires clear ownership, appropriate controls and an understanding of where AI should and should not be used.
Where it can fit
Generative AI becomes most useful when it is connected to a specific workflow, information environment and measurable outcome.
Enterprise knowledge
Help people find, summarize and work with information distributed across organizational knowledge sources.
Customer support
Assist with knowledge retrieval, response drafting, classification and support workflows.
Developer productivity
Support coding, documentation, testing, debugging and software engineering workflows.
Content operations
Generate, transform and adapt content while keeping human review and organizational controls in the workflow.
Intelligent workflows
Combine models with business rules, APIs, retrieval and human decisions to automate selected multi-step processes.
Related technology intelligence
Explore the technologies and engineering perspectives that connect generative AI to modern digital systems.
Engineer AI around the real problem
If you are evaluating generative AI, designing an AI-enabled product or deciding where intelligent capabilities belong in your technology architecture, begin with the problem, the information and the system around the model.
Explore what this technology can make possible.
Technology becomes valuable when it is connected to the right architecture, data, software and business context.