Generative artificial intelligence is a technology that can automatically create content such as text, images, audio, video, and code. By learning deep patterns and structures from massive datasets, it equips machines with human-like creative capabilities, enabling them to rapidly generate high-quality, diverse original content based on user natural language commands or prompts. Generative AI is evolving from an efficiency tool into an engine for business innovation, helping enterprises and developers significantly reduce content production costs, shorten creative cycles, and unlock new scenarios such as personalized interaction and automated development.

In traditional business models, content production tasks such as copywriting, creative design, and software development rely heavily on human experts, resulting in long creative cycles, high costs, and a lack of on-demand scalability. As enterprise digital transformation deepens, the market demand for personalized, scaled, and real-time content has grown explosively, making human resource bottlenecks a primary obstacle to business innovation. Meanwhile, key breakthroughs in large models have demonstrated exceptional capabilities in language comprehension, logical reasoning, and multi-modal generation, making it possible for machines to participate in high-value creation. How to seize this technological opportunity and upgrade content productivity from manual crafting to intelligent manufacturing has become a shared concern for enterprises. Generative AI is the core solution to this challenge. Leveraging the generative capabilities of large models, it directly translates natural language commands into expected outputs, allowing everyone to harness professional creative capabilities and reshaping the content supply chain.

• Excellent content generalization

  Generative AI can generalize highly diverse, logically consistent new samples based on limited training data across multiple modalities such as text, images, video, and audio, making it suitable for a wide variety of creative business scenarios.

• Flexible task adaptation

  Through prompt engineering, fine-tuning, or plugins, generative AI can rapidly adapt to various tasks such as translation, summarization, question answering, code generation, and design assistance, eliminating the need to build models from scratch for every new task.

• End-to-end scalability

  Cloud-based generative AI services are typically delivered as APIs or managed instances, supporting on-demand elastic scaling to ensure consistent user experience and performance guarantees from prototype validation to production-scale inference.

• Close human-machine collaboration

  Generative AI offers capabilities such as interactive generation, iterative completion, and controllable editing, enabling humans to fully participate in the generation process. This boosts efficiency while preserving the accuracy and creative direction critical to core business operations.

• A continuously evolving ecosystem

  Mainstream generative AI models receive ongoing updates, supporting model version control and automated evaluation to ensure users can continuously access higher-performing, more capable models without altering their application architecture. The open-source model ecosystem (with models such as Llama and DeepSeek) is developing rapidly, approaching or even surpassing closed-source models on multiple benchmarks, providing enterprises with autonomous, controllable, and low-cost deployment options.

• Declining inference costs

  Driven by model quantization, inference optimization, and open-source competition, the inference costs of generative AI have dropped significantly over the past two years, making large-scale production applications economically viable.

• Automated marketing content production

  Marketing and operations teams require massive amounts of copywriting, product descriptions, and promotional materials for e-commerce, social media, and other channels. With generative AI, they can generate multilingual marketing copy and initial ad drafts with a single click based on product details and brand style, reducing the content production cycle from days to minutes while maintaining a consistent brand tone.

• Intelligent code assistants

  Developers often face challenges such as writing repetitive code and low debugging efficiency. By embedding generative AI into the development environment, compliant code snippets, unit tests, and API documentation can be generated simply by describing requirements in natural language. This boosts development productivity, reduces human error rates, and accelerates onboarding for new team members.

• Virtual humans and digital media generation

  Industries such as gaming, film and television, and education require the production of high-quality character voices, scripts, and visuals. Leveraging the multi-modal capabilities of generative AI, creators can batch-generate lip-synced character speech, stylized illustrations, and plot text, significantly accelerating digital asset production and powering real-time virtual human interaction scenarios.

• Knowledge management and intelligent Q&A

  Enterprise customer service and knowledge management teams need to quickly extract answers from massive unstructured documents. With generative AI-powered Retrieval-Augmented Generation (RAG), applications can automatically transform uploaded policy documents and technical manuals into precise answers for high-quality 24/7 services. This lowers the workload on human agents while ensuring compliance and traceability.

• Scientific computing and data synthesis

  Research institutions in fields like drug discovery and meteorological modeling often face a scarcity of labeled training samples. Generative AI can synthesize high-quality simulated data based on limited real-world data to expand training sets, assist in generating molecular structures and enhanced satellite images, accelerate experimental iteration, and reduce data collection costs.

The development of generative AI is the result of a spiral evolution of algorithms, compute, and data. In its early stages, it relied primarily on templates and rules, lacking true creativity. With the rise of deep learning, models such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) ushered in a new paradigm for data generation, though content diversity and controllability remained limited. The introduction of the Transformer architecture completely transformed sequence modeling, giving birth to large-scale pre-trained models represented by GPT and BERT. This enabled models to capture long-range semantic dependencies, resulting in a qualitative leap in generation quality. In recent years, the continuous expansion of parameter scales combined with human feedback alignment techniques has gradually brought generative AI to practical levels in terms of logic, factuality, and safety, expanding its applications from text to multi-modalities such as images, video, and code, and driving its widespread commercial adoption.

• The Origin (2013–2014)

  To solve the generation problems of high-dimensional data (such as images) in the early days, researchers proposed VAEs (in 2013) and GANs (in 2014). Models of this era were capable of generating simple images with a certain degree of realism, but training was unstable and content controllability was limited.

• Breakthroughs in sequence modeling and self-attention (2017)

  The limitations of traditional recurrent neural networks, such as the inability to perform parallel computation and difficulties capturing long-range dependencies, spurred the birth of the Transformer architecture. The self-attention mechanism enabled models to efficiently learn global context, laying a foundation for large-scale text generation.

• The rise of pre-trained language models

  After transformer-based pre-trained models such as GPT and BERT emerged, self-supervised pre-training on massive unannotated corpora followed by fine-tuning for downstream tasks drove a qualitative transformation in the quality of text generation. Models gradually learned to follow simple prompts to generate coherent paragraphs.

• Scaling and ability emergence (2020–2022)

  With exponential growth in parameter size and training data, large language models demonstrated emergent capabilities such as in-context learning and chain-of-thought reasoning, while diffusion models achieved breakthroughs in image generation, bringing quality to genuinely usable standard. Advances in computing power and engineering optimization made the training of large generative models feasible.

• Maturation of multimodal and alignment technologies (2022–2024)

  Landmark technologies such as Diffusion Transformer (DiT), native multi-modal training, and Direct Preference Optimization (DPO) matured. This expanded models from pure text to text-image, multi-modal understanding and generation, and video generation. Sora marked a milestone, transitioning video generation from short-clip blind-box generation to long-form, native 4K video paired with synchronized audio, multi-shot storyboard segmentation, and cinematic camera angles.

  Techniques such as Reinforcement Learning from Human Feedback (RLHF) and DPO made model outputs more closely aligned with human intentions and safety guidelines, followed by reinforcement learning methods targeting verifiable reasoning tasks, such as GRPO and RLVR.

  Generative AI began to be widely integrated into cloud services as a foundational capability.

• Current status (2025–present)

  Generative AI has formed a platform-based ecosystem comprising foundational models and toolchains and has been used across a wide range of industries through APIs and low-code platforms. Capabilities such as AI agents, RAG, and model routing have driven generative AI's evolution from a standalone capability into the central hub of complex business systems. Meanwhile, model compression and inference optimization have driven inference costs down by more than two orders of magnitude over the past two years.

  AI video generation, in particular, reached production-ready levels between 2025 and 2026, with native 4K output, synchronized audio generation, and multi-shot storyboards becoming standard features in mainstream models. Dramatic cost reductions in video generation are reshaping the advertising, film pre-visualization, and short-form video creation industries.

  As applications of generative AI across various sectors have experienced explosive growth, global AI regulatory frameworks have accelerated their rollout. The high-risk rules of the EU AI Act took full effect in August 2026, and the United States and China have respectively advanced regulations concerning AI-generated content labeling, deepfakes, and copyright protection. Compliance capabilities are becoming core competitive advantages of generative AI products.

From an engineering perspective, a complete generative AI system can typically be broken down into four closely collaborating key components: a data foundation, a model engine, a service gateway, and intelligent applications. The data foundation provides high-quality training data and retrieval knowledge; the model engine powers core generation capabilities; the service gateway encapsulates complex models into secure, meterable APIs; and the intelligent application delivers business value directly to end users.

• Data foundation

  The data foundation is responsible for the collection, cleaning, annotation, anonymization, and storage of raw multimodal data. It also includes vector databases to support converting enterprise private knowledge into searchable representations, providing semantically relevant context for RAG. The quality and richness of the data foundation directly determine the model's capabilities and the factual accuracy of applications.

• Model engine

  The model engine comprises large-scale pre-trained foundational models (such as LLMs and diffusion models) as well as industry-specific models derived from fine-tuning and alignment. This component runs model weights through training or inference frameworks, executing operations such as autoregressive decoding or iterative denoising to transform input conditions into text, images, code, and more. The model engine is the wellspring of creativity for the entire system.

• Service gateway

  Serving as a central hub between the model engine and upper-layer applications, it provides unified APIs, authentication, traffic control, request routing, and content filtering. It abstracts away the invocation details of underlying heterogeneous models, converting them into standardized atomic services while supporting billing, monitoring, and canary releases to ensure services are secure, compliant, and observable.

• Intelligent applications

  This layer is oriented to end-user use cases. Examples include conversational assistants, copywriting generators, design tools, and coding assistants. The application layer combines and orchestrates underlying atomic services, designs user experience, collects user feedback, and flows data and performance metrics back to form a closed loop system.

Collaboration: The data foundation provides training data and retrieval knowledge to the model engine; the inference capabilities produced by the model engine are exposed through the service gateway; intelligent applications call models through the gateway and log user operations; and application feedback ultimately flows back into the data foundation and model engine to drive continuous optimization and iteration. The entire information and control flow forms a closed loop: data feeds the model, the model produces outputs packaged as services, applications consume those services, and feedback drives continuous optimization.

Key mechanisms: This layered architecture applies high-cohesion, low-coupling design principles. Each layer can be selected, scaled, and evolved independently. For example, when a foundation model is upgraded, the application layer can switch seamlessly without disruption. When new knowledge is added to the data layer, it takes effect immediately through RAG. There is no new model retraining required. This decoupling significantly reduces system complexity, which makes changes less risky.

Based on this architectural division of labor, generative AI systems achieve an end-to-end connection from raw data to business value. They provide users with a foundational AI content generation infrastructure that is custom-tailored, secure and controllable, continuously evolving, and adaptive to the GenAI project requirements of various industries and scales.

For different modalities, generative AI relies on different core neural network architectures:

• Transformer architecture (for text and multimodal understanding/generation)

  Self-attention mechanism: This is the soul of the Transformer. It allows the model to "see" all other words in the sentence while processing the current word and calculate the correlation weights between them. This enables the model to understand long-range contextual logic (such as pronoun reference and causal reasoning).

  Applications: Large models such as GLM (Zhipu AI), DeepSeek, Kimi, GPT, Llama, and Gemini are all built on this architecture.

• Diffusion models (for image and video generation)

  Forward process (noising): The model progressively adds random Gaussian noise to a clear image until it becomes pure static. The model records this degradation process.

  Reverse process (denoising): The model learns how to remove noise step by step from a noisy image to restore a clear picture. When generating a new image, the model starts with a random noise field and gradually refines it into an image that matches the prompt.

  Applications: Tongyi Wanxiang (image generation), Vidu (video generation), Midjourney, Stable Diffusion, Sora

• Generative adversarial networks and variational autoencoders

  Traditional generation technologies. GANs improve generation quality through an adversarial game between a "generator" and a "discriminator," while VAEs generate content by compressing data into a latent space and then decoding it back. Although gradually superseded by diffusion models in cutting-edge visual generation, they still find applications in specific domains.

Generative AI services operate as serialized pipelines that progressively transform natural language inputs into high-quality generated content. Its core steps are executed collaboratively between the gateway and the model engine, balancing safety, efficiency, and quality. The process is as follows:

• Inputs

  An input is a request submitted by a user through an API, SDK, or console. Typical inputs include prompts (for example, "Write a short essay on environmental protection"), generation parameters (such as the maximum length and temperature coefficient), and authentication credentials. Requests are typically initiated actively by clients or business systems, triggering services in real time.

• Key steps

  1. Authentication & quota verification

     The system authenticates user identity, validates API keys, and checks whether call counts or concurrency limits have been exceeded. Any illegal or over-quota requests are intercepted to ensure fair resource allocation.

  2. Prompt enhancement and retrieval

     Depending on configured templates or RAG strategies, relevant enterprise internal knowledge and document snippets are retrieved from the vector database. This authoritative information is organized into the final input context to reduce hallucinations and improve factual accuracy.

  3. Model inference

     The enhanced prompt is fed into the model engine. For LLMs, it executes autoregressive decoding, predicting the next token step-by-step until an end-of-sequence token is reached; for diffusion models, it performs multi-step denoising based on text encoding and timesteps. This phase fully utilizes GPU/NPU clusters for high-speed parallel computation.

  4. Post-processing and filtering

     The model's raw output is inspected and cleaned for sensitive words, policy violations, formatting anomalies, and injection attacks, applying debiasing and detoxification rules. Outputs requiring further checks also undergo format validation and structured extraction to ensure the delivered content is compliant and usable.

  5. Output delivery and logging

     The final generated content is packaged into standard formats such as JSON and returns it to the caller. Simultaneously, request metadata (latency, token usage, status) is written to the logging system for subsequent billing, monitoring, and operational analysis.

Classification dimensions: content modality and task form.

Classification basis: the data forms of the model's inputs and outputs, and the type of core generation problem being solved.

These categories are not mutually exclusive. In practice, cloud services may integrate multiple capabilities and provide composite generation functions through APIs.

Huawei Cloud MaaS and ModelArts provide foundational language, vision, multimodal, and scientific computing models, as well as domain-specific models for industries such as government, finance, and manufacturing. You can directly invoke high-quality generation capabilities through APIs or managed services, or fine-tune models using your own data within a securely isolated, dedicated environment to ensure data sovereignty and compliance. In addition, retrieval-augmented generation and knowledge bases enable you to connect models to your internal business systems for more accurate and reliable results.

• MaaS provides LLMs for copywriting, coding assistance, and intelligent Q&A, vision models for text-to-image generation and image editing, and multimodal models for cross-modal retrieval and description of images and text. For details about the functions and specifications, see the MaaS Documentation.

• ModelArts provides an end-to-end workflow from data labeling and model training to inference and deployment. It allows you to combine foundation models with your private data to easily build custom generation services. You can quickly create a training job to experience the capabilities described here by referring to Getting Started with ModelArts.