As businesses advance their LLM deployments from pilot to production, the emphasis needs to be on adopting real-time, adaptive, and automated data frameworks. These enable models to remain relevant and accurate without excessive manual intervention.
Enterprises have been exploring transformative use cases for large language models (LLMs) for some time now. They have discovered and defined clear business problems that artificial intelligence (AI) can solve. However, scaling applications from pilot projects to full-scale implementation often presents challenges.
Developers typically face obstacles related to operational data management and model maintenance at this point. These issues aren’t always clear in proofs of concept and are revealed as operational headaches during rollout stages. Enterprises function in complex environments where data is constantly changing. To extract maximum impact from LLMs, organizations need to learn how to harness their most up-to-date private company data by contextualizing, indexing, and retrieving at operational speed. We are increasingly seeing businesses turn to Live AI to achieve this.
Traditionally, building live, real-time data pipelines would normally require continuous engineering oversight, which is expensive in terms of time and money. But what if we told you there was another way?
Transitioning from batch uploads to real-time data feeds
One of the primary issues when moving LLM applications from pilot to production is the reliance on static batch data uploads. Batch processing means LLM outputs are only as accurate and timely as the most recent batch data. This approach is suitable in some cases, and popular models like ChatGPT, for example, operate like this. Outputs are likely to be a little out of date, but overall, it won’t be the end of the world for the typical user.
The limits to this approach come when applications need to be responsive to changing data in real-time, such as for status alerting or monitoring datasets that evolve rapidly. LLMs built on static datasets do not learn and adapt continuously. They lack the constant state of learning and unlearning that humans depend on to inform decisions. Relying solely on unmoving data means that models cannot update, amend, or self-correct their knowledge when information becomes incorrect or outdated. An LLM tasked with regulatory monitoring, for example, may miss critical updates while awaiting the next data upload, rendering its insights obsolete.
Real-time data feeds must be embraced for LLMs to be most useful and impactful. By integrating continuous data pipelines, models’ accuracy is enhanced as they react to the most recent data, providing crucial insights based on current information.
The transition from static to live pipelines involves creating hybrid systems that leverage batch processing and live data connectors or API-based feeds. This dual approach eliminates the work of plumbing the data pipeline with integrated, transformed data to feed models. It also allows LLMs to stay continuously updated and learn and unlearn data as it evolves, greatly enhancing their capabilities.
See also: Bridging the Gap: Scaling GenAI for Real Business Growth & Impact
Overcoming integration and context challenges
Building a robust streaming data integration framework is by no means easy, and blending data sources is tricky. Complexities in data preparation, cleaning, classification, and context alignment need to be addressed. Different pipeline stages, such as efficient classification and metadata addition, are essential in cleaning and deduplicating data. Context across semi-structured and structured data streams must be consistent to avoid context mismatch, which can lead to inaccurate outputs.
Monitoring these responsibilities through structured LLMOps processes provides technical leads with a reasonable indication of data quality, enabling reliable performance. Implementing this approach from the outset of a project is much easier than reverse engineering a batch upload model once it’s been scaled.
Reducing the engineering burden
The shift towards real-time data requires sophisticated data infrastructure, which typically places a heavy burden on data engineering resources. Historically, maintaining a live pipeline has involved a dedicated, specialized team in managing continuous batch uploads and ensuring data accuracy. This requirement can present a barrier to unlocking real-time applications due to the significant cost and resource investment it demands and the rarity of the skillset.
Therefore, moving to a more modern strategy is essential. This involves designing a data pipeline from the very beginning that is capable of automatically integrating, transforming, and feeding data into the LLM without constant manual intervention. By adopting frameworks that allow for automation and intelligent data management, businesses can significantly reduce the need for full-time data engineers dedicated to maintaining these models. Rather than removing the need for data engineers at all, this strategy removes repetitive, time-consuming manual tasks from their workload and allows them to focus on innovating to unlock further AI-driven efficiencies.
The future is in real time
As businesses advance their LLM deployments from pilot to production, the emphasis needs to be on adopting real-time, adaptive, and automated data frameworks. These enable models to remain relevant and accurate without excessive manual intervention. This ultimately ensures effective and efficient scaling of AI solutions with enterprise systems.
By thoughtfully designing these data systems and adopting a blend of batch and live processing, organizations can overcome the barriers that developers face when expanding AI applications in their organizations.
