COMMISSIONED: Whether you’re using one of the leading large language models (LLM), emerging open-source models or a combination of both, the output of your generative AI service hinges on the data and the foundation that supports it.
The right mix of models combined with the right data, architecture and solutions can provide GenAI services that retrieve critical contextual information at lightning speed while reducing inaccuracies.
Balancing the right mix of technologies and techniques is a delicate dance. Technology implementation challenges, security concerns and questions about organizational readiness rank among the top barriers to success, according to recent Bain & Company research.
What architecture and toolset will help me build and test a digital assistant? How do I safely integrate my enterprise information into models? What platform will provide the best performance? And how do I avoid overprovisioning, a common sin from past deployment efforts?
These are frequently asked questions organizations must consider. There are no cookie-cutter approaches for your organization. Yet early trailblazers have found fortune with certain technologies and approaches.
RAG – fueled by a vector database
From digital assistants to content creation and product design, many corporations choose to tap their own corporate data for GenAI use cases. Among the most popular techniques for this is retrieval-augmented generation (RAG).
An alternative to fine-tuning or pre-training models, RAG finds data and documents relevant to a question or task and provides them as context for the model to provide more accurate responses to prompts.
A crucial ingredient of RAG applications is the vector database, which stores and retrieves relevant, unstructured information efficiently. The vector database contains embeddings, or vector representations, of documents or chunks of text. RAG queries this content to find contextually relevant documents or records based on their similarity to keywords in a query.
The pairing of vector database and RAG is a popular combination. Research from Databricks found that use of vector databases soared 376 percent year over year, suggesting that a growing number of companies are using RAG to incorporate their enterprise data into their models.
Synthetic data protects your IP
You may have reservations about trying techniques and solutions with which you and your team may not be familiar. How can you test these solutions without putting your corporate data at risk?
One option organizations have found success with is synthetic data, or generated data that mimics data from the actual world. Because it isn’t real data it poses no real risk to corporate IP.
Synthetic data has emerged as a popular approach for organizations looking to support the development of autonomous vehicles, digital twins for manufacturing and even regulated industries such as health care, where it helps avoid compromising patient privacy.
Some GenAI models are partially trained on synthetic data. Microsoft’s Phi-3 model was partially trained on synthetic data and open source options are emerging regularly.
Regardless of whether you use synthetic data to test or real corporate IP, content created by GenAI poses risks, which is why organizations must keep a human in the loop to vet model outputs.
To the lakehouse
RAG, vector DBs and synthetic data have emerged as critical ingredients for building GenAI services at a time when 87 percent of IT and data analytics leaders agree that innovation in AI has made data management a top priority, according to Salesforce research.
But where is the best place to draw, prep and manage that data to run through these models and tools?
One choice that is gaining steam is a data lakehouse, which abstracts the complexity of managing storage systems and surfaces the right data where, when and how it’s needed. A data lakehouse forms the data management engine for systems that detect fraud to those that derive insights from distributed data sources.
What does a lakehouse have to do with RAG and vector databases? A data lakehouse may serve as the storage and processing layer for vector databases, while storing documents and data RAG uses to craft responses to prompts.
Many organizations will try many LLMs, SLMs and model classes, each with their trade-offs between cost and performance, and many will attempt different deployment approaches. No one organization has all the right answers – or the wherewithal to pursue them.
That’s where a trusted partner can help.
Dell offers the Dell Data Lakehouse, which affords engineers and data scientists self-service access to query their data and achieve outcomes they desire from a single platform.
Your data is your differentiator and the Dell Data Lakehouse respects that by baking in governance to help you maintain control of your data on-premises while satisfying data sovereignty requirements.
The Dell Data Lakehouse is part of the Dell AI Factory, a modular approach to running your data on premises and at the edge using AI-enabled infrastructure with support from an open ecosystem of partners. The Dell AI Factory also includes professional services and use cases to help organizations accelerate their AI journeys.
Brought to you by Dell Technologies.
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