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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)

5 Top Use Cases For Driving Business With Data Hub Architecture 

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      * [Questions to SQL Queries](https://www.gigaspaces.com/how-it-works/questions-into-sql-queries)
      * [Asked & Answered in Natural Language](https://www.gigaspaces.com/how-it-works/asked-answered-natural-language)
      * [Multiple Data Sources](https://www.gigaspaces.com/how-it-works/supported-data-sources)
      * [Proactive AI Governance](https://www.gigaspaces.com/how-it-works/proactive-ai-governance)
    * [![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 

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      * [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)
    * By Industry

      * [Logistics](https://www.gigaspaces.com/case-studies/optimizing-supply-chain-with-ai-powered-insights)
      * [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)
      * [Energy](https://www.gigaspaces.com/case-studies/boosting-operations-reducing-penalties)
      * [Hospitality](https://www.gigaspaces.com/case-studies/restaurant-increase-profit-erag)
    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2025/02/Company-menu-thumb.jpg)](https://go.gigaspaces.com/webinarod/ecurely-unlocking-structured-data-for-enterprise-ai-0)

Monkey See, AI Do - All about CUA

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    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2025/02/Resources-menu-thumb.jpg)](https://www.gigaspaces.com/blog/from-rag-to-tag)

Taking the AI leap from RAG to TAG

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        * [GenAI Catalyst](https://www.gigaspaces.com/products/genai-catalyst)
        * [Instant Data](https://www.gigaspaces.com/products/instant-data)
        * [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-lobby)
      * [XAP](https://www.gigaspaces.com/products/xap-imdg)
    * Solutions for

      * [Pharma](https://www.gigaspaces.com/products/erag/pharma)
      * [Procurement](https://www.gigaspaces.com/products/erag/procurement)
    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2025/02/Product-menu-thumb.jpg)](https://go.gigaspaces.com/webinar/why-conventional-rag-falls-short)

Conventional RAG Falls Short with Enterprise Databases

[Watch the Webinar![icon](/wp-content/uploads/2023/04/Group-30743.png)](https://go.gigaspaces.com/webinar/why-conventional-rag-falls-short)

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

    * Business 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)
      * Industry Solutions
      * [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)
    * [![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)

5 Top Use Cases For Driving Business With Data Hub Architecture 

[Watch the Webinar![icon](/wp-content/uploads/2023/04/Group-30743.png)](https://go.gigaspaces.com/5-top-use-cases-for-driving-business-with-data-hub-architecture)

  * How it Works![](https://www.gigaspaces.com/wp-content/uploads/2022/11/Path-16484.svg)

    * [eRAG Technology Overview](/how-it-works/erag-how-it-works)

      * [AI-Ready, IT-Friendly](https://www.gigaspaces.com/products/ai-ready)
      * [Semantic Reasoning](https://www.gigaspaces.com/how-it-works/semantic-reasoning)
      * [Questions to SQL Queries](https://www.gigaspaces.com/how-it-works/questions-into-sql-queries)
      * [Asked & Answered in Natural Language](https://www.gigaspaces.com/how-it-works/asked-answered-natural-language)
      * [Multiple Data Sources](https://www.gigaspaces.com/how-it-works/supported-data-sources)
      * [Proactive AI Governance](https://www.gigaspaces.com/how-it-works/proactive-ai-governance)
    * [![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)

    * 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)
    * By Industry

      * [Logistics](https://www.gigaspaces.com/case-studies/optimizing-supply-chain-with-ai-powered-insights)
      * [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)
      * [Energy](https://www.gigaspaces.com/case-studies/boosting-operations-reducing-penalties)
      * [Hospitality](https://www.gigaspaces.com/case-studies/restaurant-increase-profit-erag)
    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2025/02/Company-menu-thumb.jpg)](https://go.gigaspaces.com/webinarod/ecurely-unlocking-structured-data-for-enterprise-ai-0)

Monkey See, AI Do - All about CUA

[Watch Webinar![icon](/wp-content/uploads/2023/04/Group-30743.png)](https://go.gigaspaces.com/webinarod/ecurely-unlocking-structured-data-for-enterprise-ai-0)

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    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2025/02/Resources-menu-thumb.jpg)](https://www.gigaspaces.com/blog/from-rag-to-tag)

Taking the AI leap from RAG to TAG

[Read the Blog![icon](/wp-content/uploads/2023/04/Group-30743.png)](https://www.gigaspaces.com/blog/from-rag-to-tag)

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

    * Our Company

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    * col2

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      * [Technology Partners](https://www.gigaspaces.com/partners-lobby/technology)
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GigaSpaces, IBM & AWS make AI safer

[Read How![icon](/wp-content/uploads/2023/04/Group-30743.png)](https://www.gigaspaces.com/news/gigaspaces-partners-with-ibm-and-aws-to-deliver-accurate-safe-genai-with-dbs)

  * [Book a Demo](https://www.gigaspaces.com/book-a-demo)



  * Products
    * Our Products
      * [eRAG](https://www.gigaspaces.com/products/erag)
        * [GenAI Catalyst](https://www.gigaspaces.com/products/genai-catalyst)
        * [Instant Data](https://www.gigaspaces.com/products/instant-data)
        * [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
      * [Pharma](https://www.gigaspaces.com/products/erag/pharma)
      * [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
    * [eRAG Technology Overview](https://www.gigaspaces.com/how-it-works/erag-how-it-works)
      * [AI-Ready, IT-Friendly](https://www.gigaspaces.com/products/ai-ready)
      * [Semantic Reasoning](https://www.gigaspaces.com/how-it-works/semantic-reasoning)
      * [Questions to SQL Queries](https://www.gigaspaces.com/how-it-works/questions-into-sql-queries)
      * [Asked & Answered in Natural Language](https://www.gigaspaces.com/how-it-works/asked-answered-natural-language)
      * [Multiple Data Sources](https://www.gigaspaces.com/how-it-works/supported-data-sources)
      * [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)
    * By Industry
      * [Logistics](https://www.gigaspaces.com/case-studies/optimizing-supply-chain-with-ai-powered-insights)
      * [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)
      * [Energy](https://www.gigaspaces.com/case-studies/boosting-operations-reducing-penalties)
      * [Hospitality](https://www.gigaspaces.com/case-studies/restaurant-increase-profit-erag)
  * Resources
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    * [Videos](https://www.gigaspaces.com/resources/videos)
    * [Q&As](https://www.gigaspaces.com/answers)
    * [Whitepapers & Brochures](https://www.gigaspaces.com/resources/brochures)
    * [Customer Case Studies](https://www.gigaspaces.com/resources/case-studies)
    * [Events](https://www.gigaspaces.com/resources/events/)
    * [Glossary](https://www.gigaspaces.com/data-terms)
    * [FAQs](https://www.gigaspaces.com/products/erag/faq)
    * [Blog](https://www.gigaspaces.com/blog)
    * [Technical Documentation](https://docs.www.gigaspaces.com/latest/landing.html)
  * Company
    * [About](https://www.gigaspaces.com/about)
    * [Customers](https://www.gigaspaces.com/customers)
    * [Management](https://www.gigaspaces.com/leadership)
    * [Board Members](https://www.gigaspaces.com/board-members)
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    * [Press Releases](https://www.gigaspaces.com/press-releases)
    * [Careers](https://www.gigaspaces.com/career-lobby)
    * [Partners](https://www.gigaspaces.com/partners-lobby)
      * [OEM Partners](https://www.gigaspaces.com/partners-lobby/oem)
      * [System Integrators](https://www.gigaspaces.com/partners-lobby/system-integrators)
      * [Technology Partners](https://www.gigaspaces.com/partners-lobby/technology)
      * [Value Added Resellers](https://www.gigaspaces.com/partners-lobby/value-added-resellers)
    * Support & Services
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  * [Pricing](https://www.gigaspaces.com/pricing)
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# RAG Pipelines

[More Glossary Terms](https://www.gigaspaces.com/data-terms)

In the age of generative AI, traditional language models are hitting their limits when it comes to accuracy and context. That’s where Retrieval-Augmented Generation (RAG) pipelines step in. RAG combines the power of large language models (LLMs) with external knowledge retrieval, enabling smarter, more context-aware answers. 

Whether you’re building internal enterprise chatbots or domain-specific copilots, understanding and deploying RAG pipelines for production at scale can be a game-changer for AI-driven applications.

## What are RAG Pipelines?

RAG pipelines are a hybrid architecture that pairs a retrieval system (like a search engine or vector database) with a generative language model. The pipeline works in two stages:

  1. Retrieval – Relevant documents or data are fetched from an external knowledge base using the input query.
  2. Generation – The retrieved content is passed to an LLM (like GPT or similar) to generate a response grounded in the retrieved knowledge.



[The magic of RAG](https://www.gigaspaces.com/data-terms/retrieval-augmented-generation) lies in bridging the gap between static model knowledge and dynamic, up-to-date information. This is particularly helpful when factual accuracy, context sensitivity, and real-time updates are key.

## The Components of RAG Pipelines

When building RAG pipelines, there are three essential components:

### **Retriever**

The retriever identifies documents or text snippets from a corpus that are semantically relevant to the user’s query. Common retriever types include:

  * Dense retrievers (such as using embeddings and vector similarity search)
  * Sparse retrievers (for instance, BM25, TF-IDF)



Retrievers use tools like FAISS, Weaviate, Pinecone, or Elasticsearch to fetch top-N results.

### **Reader or Generator**

The reader or generator is usually a large language model that processes the retrieved documents and crafts a coherent, contextually relevant answer. Examples include:

  * OpenAI GPT models
  * Cohere, Claude, or open-source LLMs like Mistral and LLaMA



### **Knowledge Store**

This can be any [structured or unstructured data source](https://www.gigaspaces.com/question/what-is-the-architecture-of-a-rag-system) (internal documents, product manuals, websites, or data lakes) transformed into embeddings and stored in a searchable format. Proper document chunking and metadata tagging significantly impact retrieval accuracy.

Additional elements like prompt engineering, context window management, ranking filters, and feedback loops can enhance overall pipeline quality.

## Designing RAG Pipelines

When designing RAG pipelines, a thoughtful architecture matters more than brute-force compute. Here’s what to keep in mind:

### **Chunking and Preprocessing**

Documents need to be split into manageable units or “chunks” for optimal indexing and retrieval. Chunk size affects relevance, latency, and token usage. Common strategies include:

  * Sentence or paragraph-based chunking
  * Sliding windows for overlapping context
  * Metadata tagging for filtering by source, date, or document type



### **Embedding Model Selection**

The quality of your retriever depends heavily on the embeddings you use. Open-source models like Sentence Transformers, or proprietary ones from OpenAI, help translate text into vectors with contextual meaning.

### **Prompt Design**

Prompt engineering is critical for maximizing the relevance and faithfulness of generated answers. Include system-level instructions (e.g., "Answer only based on provided documents") and add citations or sources when needed.

### **Evaluation and Metrics**

Evaluating RAG pipelines requires a mix of quantitative and qualitative benchmarks. Key metrics include:

  * Precision@K: How many of the top-K retrieved documents are relevant
  * BLEU, ROUGE, and METEOR: Measure similarity between generated and reference answers
  * Faithfulness: Is the output grounded in the retrieved content?
  * Latency: Can the system operate in real-time or near real-time?



User feedback loops, A/B testing, and continual fine-tuning are essential for improving real-world performance.

## Use Cases of RAG Pipelines

RAG pipelines have wide-ranging applications across industries. Some standout use cases include:

**Customer Support Chatbots:** Deploy intelligent bots that can pull answers from product manuals, helpdesk tickets, and knowledge bases without hallucinating or relying on outdated model memory.

**Enterprise Search:** Instead of sifting through thousands of documents, users can ask natural language questions and get answers synthesized from internal wikis, policies, and archives.

**Healthcare & Legal Research:** RAG pipelines can retrieve and summarize medical guidelines or legal precedents based on complex queries, providing fast, informed, and legally compliant outputs.

**Developer Documentation Assistants:** Enhance productivity by letting developers ask coding questions and get answers grounded in specific API docs or technical manuals.

**Compliance & Risk Analysis:** Pull contextual information from regulatory documents to automate compliance checks and risk assessments across changing jurisdictions.

## **Deploying RAG Pipelines for Production at Scale**

Moving from prototype to production brings new challenges. For deploying RAG pipelines for production at scale, consider the following best practices:

  * **Scalability:** Choose retrieval and LLM components that can scale horizontally, especially under high query volumes.
  * **Latency Optimization:** Minimize inference times by batching retrieval requests, caching embeddings, and using faster LLMs or quantized models.
  * **Monitoring & Logging:** Track retrieval relevance, model output quality, and user interactions to detect drift and continuously improve.
  * **Security & Privacy:** Ensure that sensitive data is protected throughout the retrieval and generation process. Encrypt data in transit and at rest.
  * **Governance:** Establish clear policies around content sources, model guardrails, and bias mitigation to ensure responsible AI usage.



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