Generative AI stands apart from traditional AI systems by its ability to autonomously produce content such as images, text, music, and more. Unlike other AI approaches that rely on supervised learning from labeled datasets, generative AI employs techniques like neural networks and deep learning to generate entirely new data based on patterns and examples it has been trained on. This ability to create rather than just analyze data opens up a plethora of applications across industries, making it a cornerstone of innovation in today’s AI landscape.
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leewayhertz.com-Generative AI tech stack Frameworks infrastructure models and applications.pdf
1. 1/23
Generative AI tech stack: Frameworks, infrastructure,
models and applications
leewayhertz.com/generative-ai-tech-stack
Generative AI has become more mainstream than ever, thanks to the popularity of
ChatGPT, the proliferation of image-to-text tools and the appearance of catchy avatars on
our social media feeds. Global adoption of generative AI has opened up new frontiers in
content generation, and businesses have a fun way to innovate and scale. Research
by Brainy Insights states that the revenue generated from generative AI services will hit
$188.62 billion by 2032, driven by increased AI adoption across various sectors and the
desire by enterprises’ to leverage data for informed decision-making. Businesses are
exploring the endless possibilities of generative AI as the world embraces technology and
automation. This type of artificial intelligence can create autonomous digital-only
businesses that can interact with people without the need for human intervention.
As enterprises begin to use generative AI for various purposes, such as marketing,
customer service and learning, we see rapid adoption of generative AI across industries.
This type of AI can generate marketing content, pitch documents and product ideas,
create sophisticated advertising campaigns and do much more. Generative AI allows for
absolute customizability, improving conversion rates and boosting revenue for
businesses. DeepMind’s Alpha Code, GoogleLab, OpenAI’s ChatGPT, DALL-E,
MidJourney, Jasper and Stable Diffusion are some of the prominent generative AI
platforms being widely used currently.
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This technology has many use cases, including business and customer applications,
customer management systems, digital healthcare, automated software engineering and
customer management systems. It is worth noting, however, that this type of AI
technology constantly evolves, indicating endless opportunities for autonomous
enterprises. This article will take a deep dive into the generative AI tech stack to provide
readers with an insider’s perspective on the working of generative AI.
What is generative AI?
Understanding the state of generative AI
Application frameworks: The cornerstone of the generative AI stack
Why is a comprehensive tech stack essential in building effective generative AI
systems?
A detailed overview of the generative AI tech stack
Generative AI application development framework for enterprises
Things to consider while choosing a generative AI tech stack
What is generative AI?
Generative AI is a type of artificial intelligence that can produce new data, images, text, or
music resembling the dataset it was trained on. This is achieved through “generative
modelling,” which utilizes statistical algorithms to learn the patterns and relationships
within the dataset and leverage this knowledge to generate new data. Generative AI’s
capabilities go far beyond creating fun mobile apps and avatars. They are used to create
art pieces, design, code, blog posts and all types of high-quality content. Generative AI
uses semi-supervised and unsupervised learning algorithms to process large amounts of
data to create outputs. Using large language models, computer programs in generative AI
understand the text and create new content. The neural network, the heart of generative
AI, detects the characteristics of specific images or text and then applies them when
necessary. Computer programs can use generative AI to predict patterns and produce the
corresponding content. The following image depicts the rapid advancements in
Generative AI across multiple modalities, showcasing the extensive versatility of these
technologies. LLMs are playing a pivotal role, from text and speech to more complex
systems like expert and robotics applications. These models enhance tasks such as
predictive analytics, translation, and even planning and scheduling, demonstrating AI’s
capability to adapt and excel across various industries.
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Generative AI is being widely adopted across various sectors and applications due to its
versatile capabilities. Here are the key reasons why GenAI is increasingly prevalent:
Rapid adoption: GenAI technologies are quickly integrated into existing systems
due to their ease of deployment and immediate impact on efficiency and innovation.
Speed of execution: GenAI models perform tasks far surpassing human
capabilities, swiftly processing large volumes of data to deliver results in real-time.
Efficiency with data: GenAI can operate effectively even with relatively modest
amounts of data, utilizing advanced algorithms to drive output from available data.
Open source vs. proprietary: There is a growing trend toward open-source GenAI
models, which often surpass proprietary solutions in both accessibility and
community-driven enhancements. This shift promotes the widespread use and
continuous improvement of GenAI technologies.
However, it is worth noting that generative AI models are limited in their parameters, and
human involvement is essential to make the most of generative AI, both at the beginning
and the end of model training.
To achieve desired results, generative AI uses GANs and transformers.
GAN – General Adversarial Network
GANs have two parts: a generator and a discriminator.
The generative neural network creates outputs upon request and is usually exposed to
the necessary data to learn patterns. It needs assistance from the discriminative neural
network to improve further. The discriminator neural network, the second element of the
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model, attempts to distinguish real-world data from the model’s fake data. The first model
that fools the second model gets rewarded every time, which is why the algorithm is often
called an adversarial model. This allows the model to improve itself without any human
input.
Generator
Random input Real
examples
Real
examples
Real
examples
Transformers
Transformers are another important component in generative AI that can produce
impressive results. Transformers use a sequence rather than individual data points when
transforming input into output. This makes them more efficient in processing data when
the context matters. Texts contain more than words, and transformers frequently translate
and generate them. Transformers can also be used to create a foundation model, which
is useful when engineers work on algorithms that can transform natural language
requests into commands, such as creating images or text based on user description.
A transformer employs an encoder/decoder architecture. The encoder extracts features
from an input sentence, and the decoder uses those features to create an output
sentence (translation). Multiple encoder blocks make up the encoder of the transformer.
The input sentence is passed through encoder blocks. The output of the last block is the
input feature to the decoder. Multiple decoder blocks comprise the decoder, each
receiving the encoder’s features.
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The foundational technologies lie at the base of the ecosystem. These building blocks
provide the necessary computational power and data processing capabilities. Companies
specializing in hardware and cloud services, offering the robust infrastructure to train
complex AI models, fall into this category.
Hardware and chip design
At the base of the tech stack, this layer includes the hardware technologies that power AI
computations. Effective hardware accelerates AI processes and enhances model
capabilities.
Nvidia: Develops GPUs and other processors that significantly speed up AI
computations, facilitating more complex and capable AI models.
Graphcore: They have made strides with its Intelligence Processing Units (IPUs),
chips specifically engineered for AI workloads.
Intel: Offers hardware solutions that enhance the processing capabilities required
for AI model training and inference.
AMD: Provides high-performance computing platforms that support intensive AI and
machine learning workloads.
Cloud platforms
Cloud platforms provide the necessary infrastructure for building and scaling AI
applications:
Amazon AWS, Microsoft Azure, Google GCP: These cloud hyperscalers offer
extensive computational resources and full-stack AI tools, facilitating the
development, hosting, and management of AI applications.
Models layer
This layer features pre-trained, highly versatile models that can be adapted for various
applications:
OpenAI: Known for its pioneering models like GPT-3, which have set benchmarks
in the AI community.
Llama: It offers a flexible and powerful model capable of handling a range of tasks,
from translation to content generation.
Claude and Mistral: New entries to the market offering distinct capabilities in
understanding and generating human-like text, available through API access for
easier adoption.
Managed LLMs layer
Several companies provide managed large language models, offering models as a
service for ease of integration and use. These platforms provide managed LLM services
that help enterprises integrate advanced AI capabilities without managing underlying
model complexities.
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MosaicML is a fully interoperable, cloud-agnostic, and enterprise-proven platform
that enables training large AI models on your data in a secure environment. It offers
state-of-the-art MPT large language models (LLMs) and is designed for fast, cost-
effective training of deep learning models.
Frameworks and proprietary technologies
Foundational tools and proprietary systems that support AI functionalities:
OpenAI, Google’s Vertex AI, and NVIDIA are innovators in AI research and
development. OpenAI is known for its GPT models, Google’s Vertex AI provides
a platform for machine learning model development, and NVIDIA offers advanced AI
computing platforms.
Microsoft GenAI Studio: Popularly known as Azure AI studio, it is designed for
building, evaluating, and deploying generative AI solutions and custom copilots,
providing comprehensive tools to streamline the creation of AI applications.
Llama: Meta’s large language model is designed for a variety of tasks, part of
Meta’s initiative to enhance AI research and deployment capabilities.
Consultancy and strategy
Consultancy and strategy involve guiding organizations in integrating and optimizing AI
within their operational frameworks. Companies at this stage help businesses align AI
strategies with their overall objectives.
McKinsey & Company: Advises companies on leveraging AI for strategic
advantage, including operational improvements and innovation.
Bain & Company: Specializes in helping businesses implement complex AI
solutions while ensuring that technology aligns with business goals.
Development and infrastructure
This layer focuses on developing AI models and providing the necessary infrastructure for
their operation. It encompasses the tools and environments where AI models are trained,
tested, and refined. Prominent players in this layer are:
Infosys: Delivers AI-driven solutions that integrate seamlessly with enterprise
systems, enhancing business processes and customer experiences.
LeewayHertz: Offers a comprehensive suite of AI development services across
industries, leveraging the latest Generative AI technologies.
HCL: Offers AI services that help businesses implement intelligent automation and
predictive analytics.
Data layer
The data layer is essential for the functionality of generative AI, providing the necessary
infrastructure for data management and analytics. AI technologies in this layer ensure
data quality and accessibility, critical for accurate model training and execution. Major
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contributors at this stage are:
Snowflake: Provides a data warehouse solution optimized for the cloud, facilitating
the secure and efficient analysis of large datasets.
Databricks: Offers a unified platform for data engineering, collaborative data
science, and business analytics.
Splunk: Harnesses AI to enhance data processing capabilities and provide
actionable insights from big data.
Datadog: Monitors and analyzes data across cloud applications, providing insights
with real-time dashboards powered by AI.
Application layer
The application layer in the generative AI technology stack is where AI capabilities are
directly applied to enhance and streamline various business functions. This layer features
companies that have developed advanced AI-driven applications, catering to diverse
needs across different sectors. Here’s a breakdown of the categories and key companies
within the application layer
Customer support
AI-driven customer support solutions enhance user interactions and increase efficiency by
automating responses and providing data-driven insights:
ZBrain Customer Support: Offers an enterprise AI-powered platform that improves
customer service operations through automation, advanced analytics and
generative AI capabilities.
Intercom: Provides AI-first customer service solutions, including chatbots and
personalized messaging services, to deliver instant support and insights.
Coveo: Uses AI to power intelligent search solutions that improve customer service
and support.
Sales and marketing
Companies in this category utilize AI to optimize marketing strategies and sales
processes through data analysis and predictive analytics:
Einstein (by Salesforce): Leverages AI to predict customer behaviors and
personalize marketing efforts.
Jasper: Offers AI-powered tools for creating marketing content that resonates with
target audiences.
ZBrain Sales Enablement Tool: Enhances sales processes by providing AI-driven
insights and automation tools that help sales teams increase their productivity.
Operational efficiency
These applications focus on improving business operations through automation and AI-
driven process optimizations:
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DataRobot: Provides a platform for automating the creation and deployment of
machine learning models.
Pega: Integrates AI to streamline business processes and enhance decision-
making capabilities.
Software engineering
AI applications that assist in developing software, improving code quality, and reducing
development time:
Diffblue: Automates the writing of unit tests for software, improving speed and
accuracy.
Devin: Utilizes AI to assist developers in code review and bug detection processes.
While this section covered the major industry players in the generative AI tech stack, the
following section provides a detailed breakdown of the comprehensive Generative AI
technology stack, including prominent tools, application development frameworks and key
aspects to consider while choosing a generative AI tech stack.
Application frameworks: The cornerstone of the generative AI
stack
Application frameworks form the cornerstone of the tech stack by offering a rationalized
programming model that swiftly absorbs new innovations. These frameworks, such as
LangChain, Fixie, Microsoft’s Semantic Kernel, and Google Cloud’s Vertex AI, help
developers create applications that can autonomously generate new content, develop
semantic systems for natural language search, and even enable task performance by AI
agents.
Models: Generative AI’s brain
At the core of generative AI stack are Foundation Models (FMs), which function as the
‘brain’ and enable human-like reasoning. These models can be proprietary, developed by
organizations such as Open AI, Anthropic, or Cohere, or they can be open-source.
Developers also have the option to train their own models. In order to optimize the
application, developers can choose to use multiple FMs. These models can be hosted on
servers or deployed on edge devices and browsers, which enhances security and
reduces latency and cost.
Data: Feeding information to the AI
Language Learning Models (LLMs) have the ability to reason about the data they have
been trained on. To make the models more effective and precise, developers need to
operationalize their data. Data loaders and vector databases play a significant role in this
process, helping developers to ingest structured and unstructured data, and effectively
store and query data vectors. Additionally, techniques like retrieval-augmented generation
are used for personalizing model outputs.
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The evaluation platform: Measuring and monitoring performance
Choosing the right balance between model performance, cost, and latency is a challenge
in generative AI. To overcome this, developers utilize various evaluation tools that help
determine the best prompts, track online and offline experimentation, and monitor model
performance in real-time. For prompt engineering, experimentation, and observability,
along with various No Code / Low Code tooling, tracking tools, and platforms like
WhyLabs’ LangKit are used.
Deployment: Moving applications into production
Lastly, in the deployment phase, developers aim to move their applications into
production. They can choose to self-host these applications or use third-party services for
deployment. Tools like Fixie enable developers to build, share, and deploy AI applications
seamlessly.
In conclusion, the generative AI stack is a comprehensive ecosystem that supports the
development, testing, and deployment of AI applications, thereby transforming the way
we create, synthesize information, and work.
Why is a comprehensive tech stack essential in building effective
generative AI systems?
A tech stack refers to a set of technologies, frameworks, and tools used to build and
deploy software applications. A comprehensive tech stack is crucial in building effective
generative AI systems, which include various components, such as machine learning
frameworks, programming languages, cloud infrastructure, and data processing tools.
These fundamental components and their importance in a generative AI tech stack have
been discussed here:
Machine learning frameworks: Generative AI systems rely on complex machine
learning models to generate new data. Machine learning frameworks such as
TensorFlow, PyTorch and Keras provide a set of tools and APIs to build and train
models, and they also provide a variety of pre-built models for image, text, and
music generation. So these frameworks and APIs should be integral to the
generative AI tech stack. These frameworks also offer flexibility in designing and
customizing the models to achieve the desired level of accuracy and quality.
Programming languages: Programming languages are crucial in building
generative AI systems that balance ease of use and the performance of generative
AI models. Python is the most commonly used language in the field of machine
learning and is preferred for building generative AI systems due to its simplicity,
readability, and extensive library support. Other programming languages like R and
Julia are also used in some cases.
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Cloud infrastructure: Generative AI systems require large amounts of computing
power and storage capacity to train and run the models. Including cloud
infrastructures in a generative AI tech stack is essential as it provides the scalability
and flexibility needed to deploy generative AI systems. Cloud providers like Amazon
Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure offer a
range of services like virtual machines, storage, and machine learning platforms.
Data processing tools: Data is critical in building generative AI systems. The data
must be preprocessed, cleaned, and transformed before it can be used to train the
models. Data processing tools like Apache Spark and Apache Hadoop are
commonly used in a generative AI tech stack to handle large datasets efficiently.
These tools also provide data visualization and exploration capabilities, which can
help understand the data and identify patterns.
A well-designed generative AI tech stack can improve the system’s accuracy, scalability,
and reliability, enabling faster development and deployment of generative AI applications.
Here is a comprehensive generative AI tech stack.
Component Technologies
Machine learning frameworks TensorFlow, PyTorch, Keras
Programming languages Python, Julia, R
Data preprocessing NumPy, Pandas, OpenCV
Visualization Matplotlib, Seaborn, Plotly
Other tools Jupyter Notebook, Anaconda, Git
Generative models GANs, VAEs, Autoencoders, LSTMs
Deployment Flask, Docker, Kubernetes
Cloud services AWS, GCP, Azure
A detailed overview of the generative AI tech stack
The generative AI tech stack comprises three fundamental layers:
The applications layer includes end-to-end apps or third-party APIs that integrate
generative AI models into user-facing products.
The model layer comprises proprietary APIs or open-source checkpoints that
power AI products. This layer requires a hosting solution for deployment.
The infrastructure layer encompasses cloud platforms and hardware
manufacturers responsible for running training and inference workloads for
generative AI models.
Let’s dive deep into each layer.
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End-to End Apps
Users
General AI models
Cloud Platform
Compute Hardware
Specific AI models
Hyperlocal AI models
Apps without proprietary models
Compute hardware exposed to developers in a cloud deployment model
Accelerator chips optimised for model training and inference workloads
LeewayHertz
Application layer
The application layer in the generative AI tech stack as it allows humans and machines to
collaborate in new and exciting ways.These powerful applications serve as essential
workflow tools, making AI models accessible and easy to use for both businesses and
consumers seeking entertainment. The application layer enables the generation of
innovative outcomes with endless possibilities. Whether you’re looking to boost your
business’s productivity or seeking new and innovative forms of entertainment, the
application layer of the generative AI tech stack is the key to unlocking the full potential of
this cutting-edge technology.
Further, we can segregate this layer into two broad types:
End-to-end apps using proprietary models
End-to-end apps using proprietary generative AI models are becoming increasingly
popular. These software applications incorporate generative AI models into a user-facing
product and are responsible for all aspects of the generative AI pipeline, including data
collection, model training, inference, and deployment to production. The proprietary
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generative AI models used in these apps are developed and owned by a company or
organization, typically protected by intellectual property rights and not publicly available.
Instead, they are made available to customers as part of a software product or service.
Companies that develop these models have domain-specific expertise in a particular
area. For instance, a company specializing in computer vision might develop an end-to-
end app that uses a proprietary generative AI model to create realistic images or videos
where the models are highly specialized and can be trained to generate outputs tailored
to a specific use case or industry. Some popular examples of such apps include OpenAI’s
DALL-E, Codex, and ChatGPT.
These apps have a broad range of applications, from generating text and images to
automating customer service and creating personalized recommendations. They have the
potential to bring about significant changes in multiple industries by providing highly
tailored and customized outputs that cater to the specific needs of businesses and
individuals. As the field of generative AI continues to evolve, we will likely see even more
innovative end-to-end apps using proprietary generative AI models that push the
boundaries of what is possible.
Apps without proprietary models
Apps that utilize generative AI models but do not rely on proprietary models are
commonly used in end-user-facing B2B and B2C applications. These types of apps are
usually built using open-source generative AI frameworks or libraries, such as
TensorFlow, PyTorch, or Keras. These frameworks provide developers with the tools they
need to build custom generative AI models for specific use cases. Some popular
examples of these apps include RunwayML, StyleGAN, NeuralStyler, and others. By
using open-source frameworks and libraries, developers can access a broad range of
resources and support communities to build their own generative AI models that are
highly customizable and can be tailored to meet specific business needs, enabling
organizations to create highly specialized outputs that are impossible with proprietary
models.
Using open-source frameworks and libraries also helps democratize access to generative
AI technology, making it accessible to a broader range of individuals and businesses. By
enabling developers to build their own models, these tools foster innovation and creativity,
driving new use cases and applications for generative AI technology.
Model layer
The above apps are based on AI models, that operate across a trifecta of layers. The
unique combination of these layers allows maximum flexibility, depending on your
market’s specific needs and nuances. Whether you require a broad range of features or
hyper-focused specialization, the three layers of AI engines below provide the foundation
for creating remarkable generative tech outputs.
General AI models
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At the heart of the generative tech revolution lies the foundational breakthrough of
general AI models. General AI models are a type of artificial intelligence that aims to
replicate human-like thinking and decision-making processes. Unlike narrow AI models
designed to perform specific tasks or solve specific problems, general AI models are
intended to be more versatile and adaptable, and they can perform a wide range of tasks
and learn from experience. These versatile models, including GPT-3 for text, DALL-E-2
for images, Whisper for voice, and Stable Diffusion for various applications, can handle a
broad range of outputs across categories such as text, images, videos, speech, and
games. Designed to be user-friendly and open-source, these models represent a
powerful starting point for the advancements in for the generative tech stack. However,
this is just the beginning, and the evolution of generative tech is far from over.
The development and implementation of general AI models hold numerous potential
benefits. One of the most significant advantages is the ability to enhance efficiency and
productivity across various industries. General AI models can automate tasks and
processes that are currently performed by humans, freeing up valuable time and
resources for more complex and strategic work. This can help businesses operate more
efficiently, decrease costs, and become more competitive in their respective markets.
Moreover, general AI models have the potential to solve complex problems and generate
more accurate predictions. For instance, in the healthcare industry, general AI models can
be used to scrutinize vast amounts of patient data and detect patterns and correlations
that are challenging or impossible for humans to discern. This can lead to more precise
diagnoses, improved treatment options, and better patient outcomes.
In addition, general AI models can learn and adapt over time. As these models are
exposed to more data and experience, they can continue to enhance their performance
and become more accurate and effective. This can result in more reliable and consistent
outcomes, which can be highly valuable in industries where accuracy and precision are
critical.
Specific AI models
Specialized AI models, also known as domain-specific models, are designed to excel in
specific tasks such as generating ad copy, tweets, song lyrics, and even creating e-
commerce photos or 3D interior design images. These models are trained on highly
specific and relevant data, allowing them to perform with greater nuance and precision
than general AI models. For instance, an AI model trained on e-commerce photos would
deeply understand the specific features and attributes that make an e-commerce photo
effective, such as lighting, composition, and product placement. With this specialized
knowledge, the model can generate highly effective e-commerce photos that outperform
general models in this domain. Likewise, specific AI models trained on song lyrics can
generate lyrics with greater nuances and subtlety than general models. These models
analyze the structure, tone, and style of different genres and artists to generate lyrics that
are not only grammatically correct but also stylistically and thematically appropriate for a
specific artist or genre.
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As generative tech continues to evolve, more specialized models are expected to become
open-sourced and available to a broader range of users. This will make it easier for
businesses and individuals to access and use these highly effective AI models, potentially
leading to new innovations and breakthroughs in various industries.
Hyperlocal AI models
Hyperlocal AI models are the pinnacle of generative technology and excel in their specific
fields. With hyperlocal and often proprietary data, these models can achieve unparalleled
levels of accuracy and specificity in their outputs. These models can generate outputs
with exceptional precision, from writing scientific articles that adhere to the style of a
specific journal to creating interior design models that meet the aesthetic preferences of a
particular individual. The capabilities of hyperlocal AI models extend to creating e-
commerce photos that are perfectly lit and shadowed to align with a specific company’s
branding or marketing strategy. These models are designed to be specialists in their
fields, enabling them to produce highly customized and accurate outputs.
As generative tech advances, hyperlocal AI models are expected to become even more
sophisticated and precise, which could lead to new innovations and breakthroughs in
various industries. These models can potentially transform how businesses operate by
providing highly customized outputs that align with their specific needs. This will result in
increased efficiency, productivity, and profitability for businesses.
Infrastructure layer
The infrastructure layer of a generative AI tech stack is a critical component that consists
of hardware and software components necessary for creating and training AI models.
Hardware components in this layer may involve specialized processors like GPUs or
TPUs that can handle the complex computations required for AI training and inference.
By leveraging these processors, developers can process massive amounts of data faster
and more efficiently. Moreover, combining these processors with storage systems can
help effectively store and retrieve massive data.
On the other hand, software components within the infrastructure layer play a critical role
in providing developers with the necessary tools to build and train AI models. Frameworks
like TensorFlow or PyTorch offer tools for developing custom generative AI models for
specific use cases. Additionally, other software components, such as data management
tools, data visualization tools, and optimization and deployment tools, also play a
significant role in the infrastructure layer. These tools help manage and preprocess data,
monitor training and inferencing, and optimize and deploy trained models.
Cloud computing services can also be part of the infrastructure layer, providing
organizations instant access to extensive computing resources and storage capacity.
Cloud-based infrastructure can help organizations save money by reducing the cost and
complexity of developing and deploying AI models while allowing them to quickly and
efficiently scale their AI capabilities.
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Generative AI application development framework for enterprises
The GenAI application development framework for enterprises showcases a range of
strategies tailored to enhance AI capabilities within organizational structures. Here’s a
concise overview of each strategy within this framework:
RAG & context engineering
RAG and context engineering approach is widely used by enterprises focusing on
leveraging open-source frameworks such as LangChain & LlamaIndex. Our
comprehensive enterprise generative AI platform ZBrain also utilizes RAG and context
engineering approaches, critical for applications requiring nuanced AI interactions:
LangChain & LlamaIndex: These frameworks specialize in linking language
models with databases or knowledge bases, enabling context-aware responses and
decision-making capabilities.
ZBrain.ai: Offers a tailored solution that integrates contextual data processing to
streamline workflows and optimize business processes at enterprises.
Agents
Leveraging agents is a comparatively new approach to GenAI adoption at enterprises that
helps bring actionable insights. Through this approach, enterprises utilize AI-driven
interfaces that act on behalf of users, automating interactions and processes:
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Open Interpreter & Langgraph: Tools that facilitate natural language
understanding and generation, enhancing user interaction with AI systems.
Autogen Studio: Provides a platform for developing autonomous agents capable of
performing tasks based on user commands or pre-set conditions.
NVIDIA – NIMS (NVIDIA Inference Server)
NVIDIA emphasizes the integration of hardware and software to optimize AI model
performance, focusing on container-based services and industry-standard
APIs, microservice architecture, and optimizing inference engines.
Prebuilt container and helm chart: Streamlines deployment of AI models on
NVIDIA’s hardware.
Domain-specific code: Supports customized solutions that cater to specific
industry needs.
Optimized inference engines: Enhances model inference speed and efficiency,
essential for real-time applications.
Mixed approach
While not as popular, this approach merges various AI tools and methodologies to create
a flexible and robust framework:
Plugins and wrappers: These components allow for integrating third-party tools
and customizing existing systems, ensuring that enterprises can tailor AI functions
to meet their specific requirements.
The GenAI application development framework for enterprises presents a multifaceted
approach to integrating AI within organizational processes. Among these, the RAG
(Retrieval-Augmented Generation) and context engineering approach stand out as a
superior strategy for GenAI application development for these reasons:
Enhanced accuracy and relevance
Integrates real-time data retrieval with generative processes for precise,
contextually relevant outputs.
Crucial for sectors like legal, financial, and technical services, where accurate,
up-to-date information is essential for decision-making.
Dynamic learning and adaptation
Enables AI to access and utilize the latest information from updated
databases dynamically.
Prevents staleness in data, ensuring AI applications remain relevant and
effective over time.
Customizable and scalable
Provides flexibility to customize data sources and integration methods
according to specific business needs.
Allows AI systems to evolve and scale with the enterprise, aligning perfectly
with growth and change.
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Cost-effectiveness in the long term
Despite the initial investment, the approach leads to significant long-term
savings.
Automates data retrieval, minimizes manual intervention, reduces operational
costs and errors, and boosts ROI.
Among these strategies, the RAG and context engineering approach is the most effective.
It enhances the accuracy and relevance of AI outputs and ensures that the applications
are dynamic, customizable, scalable, and cost-effective over the long term. Unlike other
components that focus on automation and adaptability or NVIDIA’s hardware-centric
approach, RAG and context engineering cater to the complex, varying needs of
businesses with smarter, context-aware AI interactions. This makes it a fundamental
strategy for enterprises seeking to leverage AI for significant, impactful results across
their operations.
In the evolving enterprise technology landscape, adopting Generative AI (GenAI) requires
a strategic approach. Enterprises have a choice between relying on existing SaaS
providers and adopting a platform-driven implementation for their AI solutions. Each
approach offers distinct advantages and challenges. While SaaS options have limitations,
the platform-driven approach provides a holistic, cost-effective way of implementing
generative AI in enterprises.
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Existing SaaS providers
Traditional SaaS providers like Salesforce, Snowflake, AlphaSense, and ServiceNow
offer enterprises ready-to-use solutions. However, these platforms often come with certain
limitations:
Built-in silos: These solutions typically operate independently without integration
across other business functions, leading to siloed data and processes.
Non-holistic approach: They may not address all the nuanced needs of an
enterprise, lacking customization that aligns with specific business contexts.
High cost: While offering quick deployment, these platforms can be expensive in
the long term due to high subscription fees and limited scalability without additional
investment.
Platform-driven implementation
An alternative is a platform-driven approach, where AI solutions are custom-built, and
models are fine-tuned specifically for the organization. This method provides several
significant benefits:
Compounding IP generation: Enterprises can develop and retain intellectual
property, creating unique solutions that offer competitive advantages.
Fine-tuned for enterprise: Custom AI models are developed to meet specific
organizational needs, ensuring greater relevance and effectiveness.
Cost-effective: Over time, owning the platform can be more cost-effective than
subscribing to external services, especially when scaling operations.
Versatility for multiple use cases: A tailored platform can address many use
cases, making it a versatile tool within the enterprise.
Data sovereignty: Keeping data in-house ensures control and compliance, which
is critical for industries facing strict data protection regulations.
For enterprises looking to leverage GenAI effectively, choosing between an existing SaaS
model and a platform-driven approach depends on their specific needs, budget, and
strategic goals. A platform-driven implementation, while requiring initial investment and
development time, often yields greater long-term benefits through customization,
scalability, and data control.
Project specifications and features
It is important to consider your project’s size and purpose when creating a generative AI
tech stack, as they significantly impact which technologies are chosen. The more
important the project, the more complex and extensive the tech stack. Medium and large
projects require more complex technology stacks with multiple levels of programming
languages and frameworks to ensure integrity and performance. From a generative AI
context, the following points must be taken into consideration as part of project
specifications and features while creating a generative AI tech stack –
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The type of data you plan to generate, such as images, text, or music, will influence
your choice of the generative AI technique. For instance, GANs are typically used
for image and video data, while RNNs are more suitable for text and music data.
The project’s complexity, such as the number of input variables, the number of
layers in the model, and the size of the dataset, will also impact the choice of the
generative AI tech stack. Complex projects may require more powerful hardware
like GPUs and advanced frameworks like TensorFlow or PyTorch.
If your project requires scalability, such as generating a large number of variations
or supporting too many users, you may need to choose a generative AI tech stack
that can scale easily, such as cloud-based solutions like AWS, Google Cloud
Platform, or Azure.
The accuracy of the generative AI model is critical for many applications, such as
drug discovery or autonomous driving. If accuracy is a primary concern, you may
need to choose a technique known for its high accuracy, such as VAEs or RNNs.
The speed of the generative AI model may be a crucial factor in some applications,
such as real-time video generation or online chatbots. In such cases, you may need
to choose a generative AI tech stack that prioritizes speed, such as using
lightweight models or optimizing the code for performance.
Experience and resources
It is essential to have deep technical and architectural knowledge to select the right
generative AI tech stack. It is crucial to be able to distinguish between different
technologies and select the specific technologies meticulously when creating stacks so
that you can work confidently. The decision should not force developers to lose time
learning about the technology and be unable to move forward effectively.
Here are some ways experience and resources impact the choice of technology:
The experience and expertise of the development team can impact the choice of
technology. If the team has extensive experience in a particular programming
language or framework, choosing a generative AI tech stack that aligns with their
expertise may be beneficial to expedite development.
The availability of resources, such as hardware and software, can also impact the
choice of technology. If the team has access to powerful hardware such as GPUs,
they may be able to use more advanced frameworks such as TensorFlow or
PyTorch to develop the system.
The availability of training and support resources is also an important factor. If the
development team requires training or support to use a particular technology
effectively, it may be necessary to choose a generative AI tech stack that has a
robust support community or training resources.
The budget for the project can also influence what technology stack is used. More
advanced frameworks and hardware can be expensive, so choosing a more cost-
effective tech stack that meets the project’s requirements may be necessary if the
project has a limited budget.
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The maintenance and support requirements of the system can also impact the
choice of technology. If the system requires regular updates and maintenance, it
may be beneficial to choose a generative AI tech stack that is easy to maintain and
that comes with a reliable support community.
Scalability
Scalability is an essential feature of your application’s architecture that determines
whether your application can handle an increased load. Hence, your technology stack
should be able to handle such growth if necessary. There are two types of scaling: vertical
and horizontal. The first refers to the ability to handle increasing users across multiple
devices, whereas horizontal scaling refers to the ability to add new features and elements
to the application in the future.
Here are some factors that matter when it comes to scalability in a generative AI tech
stack:
When it comes to choosing a generative AI tech stack, the size of the dataset plays
a critical role. As large datasets require more powerful hardware and software to
handle, a distributed computing framework like Apache Spark may be essential for
efficient data processing.
Additionally, the number of users interacting with the system is another significant
consideration. If a large number of users are expected, choosing a tech stack that
can handle a high volume of requests may be necessary. This may involve opting
for a cloud-based solution or a microservices architecture.
Real-time processing is yet another consideration where the system must be highly
scalable in applications such as live video generation or online chatbots to cope
with the volume of requests. In such cases, optimizing the code for performance or
using a lightweight model may be necessary to ensure the system can process
requests quickly.
In scenarios where batch processing is required, such as generating multiple
variations of a dataset, the system must be capable of handling large-scale batch
processing. Again, a distributed computing framework such as Apache Spark may
be necessary for efficient data processing.
Finally, cloud-based solutions like AWS, Google Cloud Platform, or Azure can offer
scalability by providing resources on demand. They can easily scale up or down
based on the system’s requirements, making them a popular choice for highly
scalable generative AI systems.
Security
Every end user wants their data to be secure. When forming tech stacks, selecting high-
security technologies is important, especially when it comes to online payments.
Here is how the need for security can impact the choice of technology:
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Generative AI systems are often trained on large datasets, some of which may
contain sensitive information. As a result, data security is a significant concern.
Choosing a tech stack with built-in security features such as encryption, access
controls, and data masking can help mitigate the risks associated with data
breaches.
The models used in generative AI systems are often a valuable intellectual property
that must be protected from theft or misuse. Therefore, choosing a tech stack with
built-in security features is essential to prevent unauthorized access to the models.
The generative AI system’s infrastructure must be secured to prevent unauthorized
access or attacks. Choosing a tech stack with robust security features such as
firewalls, intrusion detection systems, and monitoring tools can help keep the
system secure.
Depending on the nature of the generative AI system, there may be legal or
regulatory requirements that must be met. For example, if the system is used in
healthcare or finance, it may need to comply with HIPAA or PCI-DSS regulations.
Choosing a tech stack with built-in compliance features can help ensure that the
system meets the necessary regulatory requirements.
Generative AI systems may require user authentication and authorization to control
system access or data access. Choosing a tech stack with robust user
authentication and authorization features can help ensure that only authorized users
can access the system and its data.
Conclusion
A generative AI tech stack is crucial for any organization incorporating AI into its
operations. The proper implementation of the tech stack is essential for unlocking the full
potential of generative AI models and achieving desired outcomes, from automating
routine tasks to creating highly customized outputs that meet specific business needs. A
well-implemented generative AI tech stack can help businesses streamline their
workflows, reduce costs, and improve overall efficiency. With the right hardware and
software components in place, organizations can take advantage of specialized
processors, storage systems, and cloud computing services to develop, train, and deploy
AI models at scale. Moreover, using open-source generative AI frameworks or libraries,
such as TensorFlow, PyTorch, or Keras, provides developers with the necessary tools to
build custom generative AI models for specific use cases. This enables businesses to
create highly tailored and industry-specific solutions that meet their unique needs and
achieve their specific goals.
In today’s competitive business landscape, organizations that fail to embrace the potential
of generative AI may find themselves falling behind. By implementing a robust generative
AI tech stack, businesses can stay ahead of the curve and unlock new possibilities for
growth, innovation, and profitability. So, it is imperative for businesses to invest in the right
tools and infrastructure to develop and deploy generative AI models successfully.