Why data engineers are the unsung heroes of the GENAI era

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As organizations add artificial intelligence to their offerings, data engineers will be integral to scaling infrastructure and governance to incorporate new models and technologies.

Translated from 3 Reasons Data Engineers Are the Unsung Heroes of GenAI , author Barr Moses.

Over the past 18 months, advances in generative AI have generated strong interest among boardrooms and business leaders. As of September, 87% of C-level executives surveyed by IDC said they were at least exploring potential use cases. According to a November 2023 Salesforce report , another 77% of business leaders are concerned that they have missed out on the benefits of GenAI.

But data leaders understand that no matter how much FOMO their CEOs experience after watching a glitzy demo, implementing the latest LLM must be thoughtful. To deliver meaningful business value, these models need to provide high-quality data—while maintaining security, privacy, and scalability.

In most organizations, there are a few key contributors already doing this work: data engineers . Given the current state of companies implementing enterprise-grade AI , data engineers will become increasingly important.

The important role of data engineers in enterprise AI

In any modern data team, data engineers are responsible for building and maintaining the infrastructure of the data stack. Their pipelines and workflows enable applications, analysts, business consumers, and data scientists to access and consume the data they need to do their jobs.

As organizations begin to layer generative AI into their products, data engineers will be integral to extending existing infrastructure and governance to include the latest models and technologies. Let’s explore three specific ways data engineers will contribute to AI success .

1. Promote RAG to improve LLM output

Currently, most organizations that are successful with GenAI are using Retrieval Augmented Generation (RAG) . This involves incorporating a knowledge source or data set into its generation process - providing the LLM with access to a dynamic database in response to prompts. For example, by fully implementing RAG, consumer-facing chatbots will be able to pull specific customer data for reference during support interactions.

For most use cases, RAG is more suitable than fine-tuning - retraining an existing LLM on a smaller, specific dataset. Fine-tuning requires significant computational resources and large amounts of data, and often involves a high risk of overfitting.

Effective implementation of RAG requires high-quality data pipelines to feed company data into AI models. Data engineers are responsible for ensuring:

The database is accurate and relevant, with regular updates and quality checks
The retrieval process is optimized and prompts are resolved using correct and contextually appropriate data
Continuously monitor and optimize data input with data observability

Preferences for RAG may change as technology advances, but for now, it's generally considered the most practical path forward for enterprise AI. It also helps reduce illusions and inaccuracies while increasing transparency for data teams.

2. Maintain security and privacy

Data engineers already play a key role in data governance, ensuring databases have appropriate built-in roles and security controls to ensure privacy and compliance. When implementing RAG, these controls need to be extended and applied consistently throughout the pipeline.

For example, a company's LLM should not use any of its customer data for its own training, while a customer-facing chatbot must confirm a user's identity and permissions before sharing sensitive data. Data engineers play a vital role in maintaining compliance with regulations and best practices.

3. Reliable, high-quality data

Ultimately, the success of GenAI depends on data quality. Even the most advanced models cannot produce useful output without continuous provision of accurate, reliable data to the LLM.

Over the past five years, leading data engineers have adopted observability tools (including automated monitoring and alerting, similar to DevOps observability software) to help improve data quality. Observability helps data teams monitor and proactively respond to events such as failed Airflow jobs, corrupted APIs, and malformed third-party data that put data health at risk. With end-to-end data lineage, teams can understand upstream and downstream dependencies.

Data engineers can provide transparency when observability tools are applied to modern AI stacks, including vector databases. Lineage allows engineers to track the source of data as it is converted into embeddings, and then use that data to generate the rich text that LLM places in front of users. This visibility helps data teams understand how LLM is operating, improve its output, and quickly troubleshoot incidents.

As CreditKarma’s VP of Engineering Vishnu Ram told us : “We need to be able to observe the data. We need to understand what data we are putting into the LLM, and if the LLM comes up with its own ideas, we need to know that — and then know what to do with it. situation. If you can't observe what goes into the LLM and what comes out, you're screwed."

Data engineers are the future of AI-driven organizations

AI technology is developing at a dizzying pace. But even as fine-tuned models and more advanced custom training become feasible for enterprises, the need to ensure data quality, security, and privacy will not change.

As organizations invest in generative AI applications, the quality and availability of their data will be more valuable than ever. This means workflows and data engineering processes may change, but their importance in organizations is just beginning.

This article was first published on Yunyunzhongsheng ( https://yylives.cc/ ), everyone is welcome to visit.