Context Windows in BI: LLM vs Traditional

July 9, 2025 By Yodaplus

In the world of Business Intelligence (BI), context is everything. Whether you are building dashboards, writing reports, or analyzing patterns, the value of your insights depends on how much relevant data the system can consider at once. Traditionally, BI tools have used structured logic, fixed queries, and defined datasets to make sense of data. But with the rise of Large Language Models (LLMs) in Artificial Intelligence solutions, a new kind of “context window” has entered the scene.

So what exactly is a context window? And how do LLM-based BI systems differ from traditional BI systems when it comes to understanding and using context?

Let’s break it down.

 

What Is a Context Window?

A context window refers to the amount of information a system can process at a given time to make decisions or generate responses.

• In traditional BI, the context window is usually defined by the report query, data model, or dashboard filters. If you want to analyze customer churn, your context might be limited to selected metrics like purchase frequency, service tickets, or recent transactions. 
• In LLM-based systems, the context window is the chunk of text, data, or metadata that the model can read in one go to produce an output. This can include multiple tables, unstructured text, historical logs, or previous conversations, all stitched together as one input. 

This ability to absorb broader and more varied context is what makes LLMs powerful for modern BI and analytics.

 

Traditional BI: Context Comes from Structure

Traditional BI platforms such as Tableau, Power BI, or Looker rely on a pre-defined structure. Data models are designed with specific relationships, and queries are written to retrieve data with precise logic. This structure offers control and repeatability, especially for standardized reporting.

Strengths of Traditional BI:

• High accuracy for known metrics and KPIs 
• Strong governance and version control 
• Integrates well with Enterprise Resource Planning (ERP systems) and Inventory management solutions 

Limitations:

• Difficult to adapt to new questions outside pre-defined scopes 
• Context is often narrow due to rigid filters or joins 
• Struggles with unstructured data like emails or PDF reports 

For example, if your Retail Technology Solutions dashboard is designed to track weekly footfall, adding a layer like weather conditions or festival schedules requires additional integration and schema updates.

 

LLM-Based BI: Context Comes from Text and Relationships

LLMs are designed to process human language. When applied to BI, they allow users to interact with data using natural language queries and get answers that include not just numbers, but also explanations, summaries, and recommendations. More importantly, they pull from multiple sources at once.

Think of a tool like GenRPT, which reads Excel files, SQL outputs, and even scanned documents. It creates a wide context window that mimics how a business analyst would read through reports and emails to build a narrative.

Strengths of LLM-based BI:

• Understands broader and unstructured data 
• Can summarize insights and create narratives 
• Useful for Smart contract development, audit trails, and cross-department queries 
• Works well in Artificial Intelligence services that combine NLP and data mining 

Limitations:

• May hallucinate or infer information incorrectly if guardrails are not in place 
• Performance depends on token limits (current LLMs have max token sizes) 
• Needs fine-tuning or grounding in enterprise datasets for accuracy 

 

A Practical Comparison

Let’s look at a scenario in Supply Chain Technology.

Use Case: A manager wants to know why the on-time delivery rate dropped in the last two weeks.

• Traditional BI: You create a query filtering the last two weeks of delivery data. You check lead times, transit durations, and maybe plot a chart. 
• LLM-Based BI: You ask, “Why did delivery performance drop recently?” The system checks delivery tables, reads logistics email threads, considers weather updates, and even parses notes from customer support. It then summarizes all this into a two-paragraph explanation. 

The difference lies in how context is gathered and used. Traditional BI asks you to define the context. LLM-based systems try to discover it for you.

 

Why Context Windows Matter in BI Today

Business decisions rely on more than just metrics. They need reasoning, trend detection, and narrative. That is where context windows in LLMs offer a major upgrade.

Here’s why the shift matters:

1. Multimodal Context
   LLMs can take inputs from different formats: structured data, unstructured text, scanned PDFs, and even images. This opens new possibilities for document-heavy domains like Document Digitization and Credit Risk Management Software. 
2. Conversational Exploration
   Users don’t need to write SQL. They just ask questions. This lowers the barrier to insights, especially in Financial Technology Solutions where users may not be tech-savvy. 
3. Cross-Domain Awareness
   LLMs can blend insights across systems: for example, linking Warehouse Management System (WMS) data with sales reports to explain a stockout. 
4. Improved Speed and Accessibility
   No need to wait for a dashboard refresh or IT support. With AI-powered Business Intelligence, context-aware agents provide immediate answers that adapt as the question changes. 

 

Handling Context Window Limits

It’s important to note that LLMs have limitations based on token size. A token is a piece of data (word or subword) that models read. GPT-4, for instance, can handle around 128,000 tokens. Beyond that, the model forgets earlier parts of the conversation.

This is being addressed through techniques like:

• Chunking: Breaking large documents into manageable sections 
• Retrieval-Augmented Generation (RAG): Searching context dynamically instead of feeding everything in 
• Memory-enhanced models: Storing long-term facts outside the context window 

These approaches make Agentic AI systems more scalable and suited for custom ERP environments where logs, invoices, and decision trees stretch across thousands of data points.

Which One Should You Choose?

The choice between traditional and LLM-based BI is not binary.

• Use Traditional BI when:
  You need precision, compliance, repeatable dashboards, and strict governance. 
• Use LLM-based BI when:
  You need flexible exploration, cross-functional summaries, document understanding, or business storytelling. 

Forward-looking enterprises are beginning to combine both. They use LLMs for dynamic querying and narrative, while still relying on structured BI tools for compliance and performance dashboards.

 

Final Thoughts

As businesses move toward more intelligent, real-time decision-making, context windows will define the effectiveness of BI tools. Traditional systems still hold value in structured reporting, but the emergence of Large Language Models is redefining how we interact with data.

From retail inventory systems to treasury management platforms, the ability to expand context through LLMs will shape the next generation of Supply Chain Optimization and Enterprise Analytics.

At Yodaplus, we’re building BI solutions that blend traditional logic with LLM-powered agents. Whether you’re managing complex data across financial platforms or digitizing trade documents, we ensure your system can not only retrieve the right data but understand the full context around it.