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Conventional RAG Falls Short with Enterprise Databases

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    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2024/07/solutons.png) Massimo Pezzini, Gartner Analyst Emeritus  ](https://go.gigaspaces.com/5-top-use-cases-for-driving-business-with-data-hub-architecture)

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      * [Pharma](https://www.gigaspaces.com/case-studies/transforming-patient-outcomes)
      * [Education](https://www.gigaspaces.com/case-studies/achieving-educational-budget-control)
      * [Retail](https://www.gigaspaces.com/case-studies/retail-intelligence-with-erag)
      * [Shipping](https://www.gigaspaces.com/case-studies/redefining-global-logistics)
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Conventional RAG Falls Short with Enterprise Databases

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    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2024/07/solutons.png) Massimo Pezzini, Gartner Analyst Emeritus  ](https://go.gigaspaces.com/5-top-use-cases-for-driving-business-with-data-hub-architecture)

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    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2025/02/How-it-works-menu.jpg)](https://go.gigaspaces.com/effective-ai-governance-wp)

Ensure GenAI compliance and governance 

[Read the Whitepaper![icon](/wp-content/uploads/2023/04/Group-30743.png)](https://go.gigaspaces.com/effective-ai-governance-wp)

  * [Success Stories![](https://www.gigaspaces.com/wp-content/uploads/2022/11/Path-16484.svg)](https://www.gigaspaces.com/case-studies)

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      * [Pharma](https://www.gigaspaces.com/case-studies/transforming-patient-outcomes)
      * [Education](https://www.gigaspaces.com/case-studies/achieving-educational-budget-control)
      * [Retail](https://www.gigaspaces.com/case-studies/retail-intelligence-with-erag)
      * [Shipping](https://www.gigaspaces.com/case-studies/redefining-global-logistics)
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      * [Hospitality](https://www.gigaspaces.com/case-studies/restaurant-increase-profit-erag)
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Monkey See, AI Do - All about CUA

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Taking the AI leap from RAG to TAG

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GigaSpaces, IBM & AWS make AI safer

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        * [Respond Proactively](https://www.gigaspaces.com/products/proactive-ai)
        * [Act Autonomously](https://www.gigaspaces.com/products/data-autonomy)
      * [Smart DIH](https://www.gigaspaces.com/products/smart-digital-integration-hub)
      * [XAP](https://www.gigaspaces.com/products/xap-imdg)
    * Solutions for
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      * [Procurement](https://www.gigaspaces.com/products/erag/procurement)
  * Solutions
    * [Digital Innovation Over Legacy Systems](https://www.gigaspaces.com/use-cases/digital-innovation-acceleration)
    * [Integration Data Hub](https://www.gigaspaces.com/technology/integration-data-hub)
    * [API Scaling](https://www.gigaspaces.com/use-cases/application-acceleration)
    * [Hybrid/Multi-cloud Integration](https://www.gigaspaces.com/use-cases/hybrid-or-multi-cloud-integration)
    * [Customer 360](https://www.gigaspaces.com/use-cases/customer-360)
    * [Retail](https://www.gigaspaces.com/retail-and-ecommerce-2)
    * [Financial Services](https://www.gigaspaces.com/financial-services)
    * [Insurance Companies](https://www.gigaspaces.com/industry-solutions/insurance)
  * How it Works
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      * [Governance](https://www.gigaspaces.com/how-it-works/proactive-ai-governance)
  * [Success Stories](https://www.gigaspaces.com/case-studies)
    * By Use Case
      * [Procurement](https://www.gigaspaces.com/use-cases/procurement-supply-chain)
      * [Operations](https://www.gigaspaces.com/use-cases/enhancing-pharmaceutical-operations)
      * [Budget Management](https://www.gigaspaces.com/use-cases/educational-organizations-budget-resources)
      * [Sales Operations](https://www.gigaspaces.com/use-cases/retail-boost-sales)
      * [Service Providers](https://www.gigaspaces.com/use-cases/contract-control-cost-savings)
      * [Utilities Management](https://www.gigaspaces.com/use-cases/cut-sla-penalties)
      * [Restaurant Management](https://www.gigaspaces.com/use-cases/power-smarter-restaurant-growth-erag)
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      * [Pharma](https://www.gigaspaces.com/case-studies/transforming-patient-outcomes)
      * [Education](https://www.gigaspaces.com/case-studies/achieving-educational-budget-control)
      * [Retail](https://www.gigaspaces.com/case-studies/retail-intelligence-with-erag)
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# What is a Foundational Model?

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A foundational model is a type of large-scale artificial intelligence (AI) model trained on broad and diverse datasets, designed to be adapted or fine-tuned for a wide array of downstream tasks. These models “form the foundation” upon which specialized AI applications are built, hence the name.

Unlike conventional [AI systems](https://www.gigaspaces.com/blog/ai-databases-design-patterns-reasoning-engines) that are trained to perform a particular function (like image classification or sentiment analysis), foundation models are trained on a general-purpose corpus (text, images, audio, or even code) allowing them to serve as versatile engines for a wide range of use cases. Once trained, these models can be fine-tuned or prompted to perform specific tasks with little or no additional training.

The term became widely recognized with the development of models like OpenAI’s GPT series and Google’s BERT, which underpin many modern AI tools and services. These AI foundation models are usually trained using self-supervised learning and scaled up to billions or even trillions of parameters, which needs massive computational power and large, diverse datasets.

## The Characteristics of Foundational Models

Foundation models are set apart by a several key characteristics that distinguish them from earlier generations of AI:

### **Scale**

The most prominent feature of large foundation models is their sheer size. With billions of parameters, these models have emergent abilities; capabilities not present in smaller models, such as reasoning, summarization, or translation.

### **Pretraining and Adaptability**

These models are trained one time on broad data using self-supervised learning methods, and later adapted to specific tasks through fine-tuning or prompt engineering. This "pretrain-then-adapt" strategy drastically reduces the effort needed to deploy AI across different domains.

### **Multimodality**

While many early models focused on a single data type (like text), modern foundation models are more and more [multimodal](https://www.gigaspaces.com/blog/multimodal-rag-boosting-search-precision-relevance), trained on, and capable of understanding multiple forms of data such as text, images, audio, and video. This makes them far more versatile and powerful.

### **Generative Abilities**

Many of today’s leading models are generative AI foundation models, that can produce coherent and contextually appropriate outputs (text, images, or even audio) based on input prompts. These capabilities are fueling innovation across industries, from content creation to coding and drug discovery.

### **Emergence and Homogenization**

As these models scale, they begin to [exhibit behaviors](https://www.gigaspaces.com/data-terms/llm-validation) that were not explicitly programmed (think solving math problems or writing code). This often leads to homogenization, where a single model architecture can serve multiple tasks, limiting the need for bespoke models.

## The Types of Foundational Models

There are several types of foundation models, categorized mainly by their data modalities and architectural design:

  * **Text-Based Models:** These include models like GPT, BERT, and T5, which are trained on large text corpora. They are commonly used for natural language processing tasks like summarization, translation, sentiment analysis, and question answering.
  * **Vision Models:** Vision Transformers (ViTs) and CLIP are examples. These models process image data and are applied in fields such as object detection, facial recognition, and autonomous driving.
  * **Multimodal Models:** Models like DALL·E, Flamingo, and Gemini fall into this category. They combine text, image, and sometimes audio data to carry out complex tasks, for instance, generating images from text descriptions or answering visual questions.
  * **Audio and Speech Models:** Foundation models trained on audio data like Whisper and AudioLM, can transcribe speech, identify speakers, and even generate music or synthetic voices.



## The Architecture of Foundational Models

While a range of architectures can be used for foundation models, the transformer architecture has become the dominant design thanks to its scalability and effectiveness across modalities.

### **Transformers**

Introduced in the seminal 2017 paper "Attention Is All You Need," transformers rely on attention mechanisms to model dependencies between input tokens. This is what allows them to handle long sequences efficiently and learn [complex patterns in data](https://www.gigaspaces.com/data-terms/data-analytics).

Transformers power text-based models like GPT and BERT, vision models (ViT), multimodal systems (such as CLIP), and audio models (like Whisper). This architectural consistency adds to the homogenization of AI model design, where the same building blocks can be used across applications.

### **Training at Scale**

Foundation models are normally trained using large-scale distributed computing across thousands of GPUs or TPUs; a process that can last weeks or even months and needs sophisticated techniques for data preprocessing, optimization, and model parallelism.

Despite the complexity, once trained, these models can be reused across hundreds of tasks, making the upfront investment worthwhile for major organizations and platforms.

## The Applications of Foundational Models

Foundation models have revolutionized the field of AI, creating tools that are general-purpose, adaptable, and capable of tackling a wide array of challenges.

**Natural Language Processing:** From chatbots and virtual assistants to legal document summarization and code generation, AI foundation models have become the engine of modern NLP solutions.

**Healthcare:** In healthcare, models are being used for clinical trial matching, radiology image analysis, and even protein folding, pushing the boundaries of biomedical innovation.

**Finance:** Foundation models can analyze vast datasets for fraud detection, risk modeling, and personalized financial advice.

**Education and Accessibility:** Tools powered by foundation models promise real-time translation, speech recognition, and content summarization, making knowledge more accessible across languages and abilities.

**Creative Industries:** Generative AI foundation [models ](https://www.gigaspaces.com/blog/challenges-and-considerations-for-architecting-machine-learning-infrastructure/)are behind the rise of AI-generated art, music, marketing content, and even game development, empowering creatives to explore new forms of expression.

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