LLMs Need Real-Time Data to Deliver Contextual Results

Large Language Models (LLMs) were initially designed for language generation, not real-time decision support, knowledge sharing, or data extraction. Their expected functionality was to predict and generate human-like text by learning statistical patterns in large amounts of language data. But with the popularization of Generative AI (GenAI) use cases, the original intent of LLMs has practically become an obstacle, especially as more companies look to apply them across every department in their organization. Because most LLMs were trained on static datasets, they’re often out of date the moment you start using them. To make LLMs succeed in enterprise settings, they need to be grounded in real-time, proprietary data that reflects what's actually happening in your business.

As companies attempt to keep up with the rapid shifts happening in the LLM space, 90 percent of enterprise AI decision makers already have concrete plans to adopt GenAI, according to Forrester. Yet, these pressures are leading companies to race to implement GenAI—or drop LLMs into their stack—just for the sake of it, treating it like a checkbox rather than a business-critical tool. Your AI investment deserves more thought and consideration, starting with models that are connected to real-time, proprietary data, including information like customer transactions, support tickets, and supply chain updates.

To build something truly useful for your organization, you’ll need to ground your LLMs in real-time context. In this post, we’ll break down the limitations of models trained only on static public data, the importance of real-time data for specific applications, and how streaming pipelines can help close the gap—plus, why a platform like Decodable can help solve all of the above.

What’s the Problem with LLMs Relying Only on Public Data?

GenAI has been getting a great deal of attention over the past 2+ years for its potential to change the ways we live and work. Being the engine of that car, LLMs are at the core of most GenAI applications and power everything from helping non-technical employees query data to auto-generating important meeting notes and action items for your current business roadmap. 

But like any engine, what you get out of it depends on what you put in. The quality of the output always boils down to the freshness of the data feeding the model. If your model is only trained on public, static data, it will fall short where it matters most, delivering relevant, reliable, and business-specific insight. 

Here are a few ways LLMs trained only on public data tend to miss the mark:

Stale, Inaccurate Insights Prone to Hallucination

Public LLMs are trained on static snapshots from the Internet, which means they quickly fall out of sync with what's happening in your business. That becomes a problem when these models are used to power real-time experiences, like explaining data trends in plain language, summarizing support tickets and next steps, and generating SEO-optimized product descriptions for e-commerce websites.

Without a continuous feed of fresh, proprietary data, public LLMs are far more likely to hallucinate or deliver irrelevant responses. They won’t reflect yesterday’s traffic spike, today’s out-of-stock product, or a policy update made an hour ago unless they’re connected to a streaming pipeline that pushes real-time data into your AI workflows.

Lack of Business-Specific Context

Having fresh data is one thing, having relevant data is another. Public sources won’t tell a model which customer segments tend to churn, what defines a high-value lead in your pipeline, or how your internal processes deviate from industry norms. That kind of context lives in your CRM, ERP, support logs, product catalog, and other proprietary systems.

If business leaders have learned anything about LLMs over the past few years, it's that they're great at producing generic answers, and sometimes, even half-truths or outright hallucinations. Answers won’t reflect your priorities, your strategy, or your customers. Worse, they can lead teams to take action on shallow or misaligned insights, even the playing field between you and your competitors, and add noise instead of clarity—only serving to weaken the strategic value of implementing AI in the first place.

Limited Real-Time Awareness

Speed is everything when people rely on a system to respond in real time. Whether it’s a customer asking about a recent order, an employee searching for an updated policy, or a merchandiser trying to understand what’s trending right now, delays can break trust.

Public LLMs aren’t designed to account for business-specific context as it evolves. Without access to real-time data, they default to surface-level answers that may sound convincing but miss what matters. Even if your model has access to high-quality internal data, it’s far less useful if it can’t process and respond to new information as it happens. In fast-moving environments, that kind of lag can create confusion, erode confidence, and make the entire experience feel disconnected from the reality of your business. 

Why Do LLMs Need Real-Time Data to Deliver Accurate Responses?

When we think of pre-trained LLMs, names like Claude, ChatGPT, and LLaMA come to mind. Once trained, these types of models generate responses based on patterns in historical data, which means they’re only as current as the last dataset they were fine-tuned on.

That’s a limitation for any business that depends on up-to-the-minute context—whether that’s recent customer activity, supply chain disruptions, or shifting market conditions. Without mechanisms to incorporate real-time data, even the most advanced models can produce outdated or inaccurate responses. That makes them unreliable in fast-changing environments, especially in use cases that demand timely, context-aware outputs.

There are many examples of where pre-trained LLMs fail for context-dependent use cases, including:

E-Commerce personalization. Personalization has been a problem for e-commerce vendors since the dot-com bubble burst, and it’s easy to see why. Imagine a customer browsing your site, ready to make a purchase, and the rule-based or ML-powered recommendation engine serves up a product that went out of stock two hours ago. Or worse, it highlights a winter coat during a summer clearance event because it doesn’t understand seasonal or regional trends.

Customer support automation. Customers’ expectations are sky-high nowadays because they've been shaped by fast, seamless experiences from industry leaders like Amazon, Apple, and Netflix. People expect instant answers, personalized recommendations, and frictionless interactions, and LLM-powered chatbots are often the first line of defense.

Fraud detection. Fraud doesn’t wait for your model to retrain. If your system is relying on data from last week — or even yesterday — it’s already behind. Attackers constantly shift tactics, and models need fresh signals like transaction anomalies, login behavior, device fingerprints, and location mismatches to detect fraud as it happens.

AI-powered search. Search has traditionally relied on keyword matching and ranking algorithms, but LLMs are now being layered in to power conversational search experiences. Instead of matching terms, these models interpret intent and respond in natural language, allowing customers to ask things like, "What’s a good gift for someone who loves hiking?" or "Do you have anything similar to the sneakers I bought last month?" That shift unlocks more intuitive, human-like discovery, but only if the model has access to current, contextual data.

What Are the Key Benefits of Real-Time Data Pipelines for AI? 

Let’s get a bit more granular about the benefits of real-time data pipelines, which provide the foundation AI systems, including LLMs, need to operate with greater speed, accuracy, and resilience.

Keeps AI Responses Accurate with Continuous Data Feeds

LLMs that rely on static or batch-processed data often generate outdated or irrelevant responses, missing the nuance of what’s happening at the moment. Real-time data pipelines address this by continuously feeding models with fresh context that improves the accuracy, clarity, and usefulness of every response—without it, quality suffers.

According to Gartner, poor data quality costs organizations an average of $12.9 million per year, a clear reminder that even the most advanced models are only as good as the data they’re fed. Real-time pipelines aren’t just about responsiveness, they’re about keeping LLMs grounded in the truth.

Fosters Scalability without Engineering Bottlenecks

LLMs can’t scale if every data update requires manual intervention. Unfortunately, many teams across industries are still facing friction trying to keep models in sync with data spread across disconnected systems, including product catalogs, customer profiles, and internal tools. They often rely on brittle custom scripts or less-than-timely batch jobs that slow everything down. 

Real-time data pipelines eliminate those bottlenecks by automating ingestion, transformation, and delivery, so LLMs can operate seamlessly without constant engineering support. With an investment in real-time data pipelines, your team will reduce deployment timelines, lower costs, and make LLMs viable beyond proof-of-concept.

Maintains Data Consistency to Prevent Errors

Beyond creating friction, poor data quality comprises every experience downstream. When the data feeding your systems is outdated, inconsistent, or poorly structured, it leads to broken customer interactions, such as a chatbot providing outdated healthcare benefits information, misquoting travel policies, or surfacing the wrong troubleshooting steps.

Using real-time data pipelines, your business prevents these hurdles by validating, cleaning, and structuring data as it moves through your systems. They catch duplicates, resolve formatting issues, and ensure that your AI systems are representative of things like your actual inventory, your policies, or customer preferences.

Decodable Helps Manage Real-Time AI with Streaming Data

Building real-time AI pipelines doesn't have to mean spinning up complex infrastructure or managing low-level streaming tools. Decodable offers a fully managed platform that helps teams ingest, transform, and route data in motion, so your AI systems can operate with the most current and reliable context possible. Here’s exactly how we can help:

Build AI-ready data pipelines with SQL, Java, or Python: Whether you're working with customer behavior, product updates, or operational signals, Decodable simplifies real-time event processing. Use standard SQL to build, transform, and route streaming data, with no deep engineering required. For more advanced needs, you can deploy custom Apache Flink jobs written in Java or Python, with inherent scaling and observability provided by our platform.

Reliable stateful stream processing: As we’ve discussed in this article, AI models need more than raw data, they need real-time context. Decodable provides stateful stream processing to power LLMs and other AI systems so they can detect patterns across streams, maintain state across events, and respond to real-time business signals with precision.

Scalable, cost-efficient infrastructure: Decodable’s architecture scales up or down automatically based on data volume. Pipelines can scale to zero when idle and instantly resume when demand increases, making it ideal for AI workloads that need both performance and flexibility.

Want to explore what’s possible with real-time AI pipelines? Join our on-demand virtual roundtable with Decodable’s Founder and CEO, Eric Sammer, and other industry leaders on the future of real-time data streaming and AI to uncover the key trends shaping the landscape.