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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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      * [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)
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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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    * Solutions for

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    * [![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)

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      * [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)
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      * [Questions to SQL Queries](https://www.gigaspaces.com/how-it-works/questions-into-sql-queries)
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      * [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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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)

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    * 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)
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    * [Blog](https://www.gigaspaces.com/blog)
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  * Company
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# What is a Context Window?

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

The context window is a technical term with real-world consequences. It refers to the amount of text, —measured in tokens, that a large language model (LLM) can “see” at once. It’s the model’s working memory. The longer the window, the more information it can process in a single go.

In practice, [the context window](https://www.gigaspaces.com/question/are-long-context-models-suitable-for-real-time-applications-gigaspaces-ai) governs how much of a conversation, document, or code snippet the model can retain without losing the thread. If you go beyond the limit, earlier parts of the input could be forgotten, truncated, or summarized. Context is not infinite.

### **Why Does the Context Window Matter in AI?**

A larger context window [LLM](https://www.gigaspaces.com/data-terms/llm-evaluation) can read more. It remembers better, and writes more coherently. Think of summarising a legal case, generating a long blog post, or answering a multi-part query. The wider the lens, the sharper the output. It can track subtle themes, revisit earlier details, and respond with greater depth.

However, context window size comes at a cost. Larger windows mean higher memory use, more computation, slower inference, and increased operational expense. They also present new vulnerabilities, —prompt injection, token bloat, or irrelevant noise. Bigger isn’t always better. Sometimes, it's just bigger. And sometimes, it's just more to manage.

## **How Do Context Windows Work?**

Tokens are the currency of context. Not words or characters, tokens. They can be short (“a”) or long (“amoral”), depending on the tokenizer used. [An AI doesn’t see](https://www.gigaspaces.com/blog/rag-technology-increasing-genai-accuracy) “Jeff drove a car.” It sees a string of token IDs like [356, 492, 13, 76].

Each token takes up space in the model’s context window. That space is limited. If the model has a 4,096-token limit and you give it a 5,000-token document, the excess will be cut off or compressed. Models trained with [Retrieval-Augmented Generation](https://www.gigaspaces.com/data-terms/rag-pipelines) (RAG) might fetch extra context on the fly, but even then, everything needs to fit inside the same window.

## **Best Practices for Working with Context Windows**

Working with context windows isn’t just about using the biggest number you can. It’s about the clever use of that space. The goal is clarity, not clutter. Whether you’re feeding in documents, prompts, or retrieved chunks, the way you structure and trim your input can make or break the model’s output. 

  * **Plan your input** : Keep prompts tight. Put the most relevant information near the beginning or end.
  * **Use RAG smartly** : Retrieve only what matters. Don’t stuff the context window with fluff.
  * **Optimize tokens** : Efficient tokenization helps stretch the available space.
  * **Test your app** : Not all models use the same context window equally well. Performance varies.



## **Common Questions About Context Window Sizes**

Most commercial large language models today offer context windows ranging from 8,000 to 128,000 tokens. Leading the pack, Google Gemini 1.5 Pro boasts the largest context window LLM on the market, supporting up to 2 million tokens, at least for now. 

But bigger isn’t always better. Simply increasing the context window doesn’t guarantee better results. Effectiveness depends heavily on your specific use case, budget, and whether the model can truly leverage the extra input. 

Keep in mind, more context also means more compute. As the context length grows, inference speed typically slows, and operational costs rise in tandem.

## **FAQs**

### **Q: How does the concept of "tokens" relate to the context window?**

Tokens are how the context window is measured. They’re not words. They’re pieces of words, or sometimes whole phrases. Different tokenizers tokenize differently. English is often more efficient than other languages like Telugu, which may require far more tokens for the same content.

On average, one token equals about 0.75 words. But don’t guess, test. Use tools like the Hugging Face Tokenizer Playground to see how many tokens your input uses.

### **Q: Why do models have a maximum context length?**

Because transformer models rely on self-attention. This mechanism calculates how each token relates to every other token in the input. It’s powerful, but costly. Double the number of tokens, and you need four times the compute.

There are technical tricks, rotary position embeddings (RoPE), sliding windows, and sparse attention, but there’s still a limit. The bigger the input, the harder it is for the model to keep up.

And it's not just your prompt that counts. Hidden system instructions, retrieved documents, formatting characters, all of it takes up space in the context window.

### **Q:****What are the implications of increasing an LLM 's context window size?**

In theory, a longer window means:

  * Better memory
  * Richer responses
  * More coherent conversations
  * Fewer hallucinations



In practice, it also means:

  * Higher compute cost
  * Slower inference
  * Increased attack surface for adversarial prompts
  * Diminishing returns if not carefully managed



It’s a trade-off. You get more power, but you pay for it, in dollars, in milliseconds, and in risk.

### **Q: What are some of the challenges associated with very long context windows?**

More isn’t always more. In fact, long context windows introduce their own problems:

  * **Cognitive laziness** : LLMs often ignore the middle of long inputs; they focus on the beginning and end - it’s human, almost
  * **Noise sensitivity** : Irrelevant input can distract or confuse the model. Garbage in, garbage out
  * **Security** : Longer context means more opportunities for prompt injection or jailbreak attacks
  * **Performance unpredictability** : Just because a model _can_ see 128,000 tokens doesn’t mean it uses them wisely



These aren’t just edge cases, they affect real-world applications, particularly those relying on high-stakes output like legal analysis, financial summaries, or medical documentation.****

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