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The role of data in generative AI
The effectiveness and versatility of any AI system, and this includes generative AI systems, depends on the quality, quantity, and diversity of data used to train its models. Let’s look at some key aspects of the relationship between data and the generative AI model.
Training data
Generative AI models are trained on massively large datasets. A model designed for text might be trained on billions of articles, while another model designed for images might be trained on millions of pictures. Large language models require vast amounts of machine learning training data if they are to generate coherent and contextually relevant content. As data is more diverse and comprehensive, the model’s ability to understand and generate a wide range of content improves.
Generally speaking, more data translates to better model outputs. With a larger dataset, generative AI models can identify more subtle patterns, resulting in more accurate and nuanced outputs. However, the quality of the data is also extremely important. Oftentimes, a smaller, high-quality dataset can outperform a larger, less relevant one.
Raw and complex data
Raw data, especially if it is complex and unstructured, may require preprocessing in the early stages of the data pipeline, before it can be usable for training. This is also the time when data is validated, to ensure it is properly representative and free from bias. This validation step is crucial for avoiding skewed or biased outputs.
Labeled data versus unlabeled data
Labeled data provides specific information about each data point (for example, textual description accompanying an image), whereas unlabeled data doesn’t include annotations like this. Generative models often work well with unlabeled data, as they are still able to learn how to generate content by understanding inherent structures and patterns.
Proprietary data
Some data is unique to a particular organization. Examples include customer order history, employee performance metrics, and business processes. Many enterprises collect this data, anonymize it to prevent sensitive PII or PHI from leaking downstream, and then perform traditional data analysis. This data holds a wealth of information that could be mined even more deeply if used to train a generative model. The resulting outputs would be tailored to the specific needs and characteristics of that business.
The role of data in RAG
As mentioned above, RAG combines the power of an LLM with real-time data retrieval. With RAG, you no longer rely solely on pre-trained data. Instead, you can execute a just-in-time pull of relevant information from external databases. This ensures that the generated content is current and accurate.
How to augment generative AI models with proprietary data
When working with generative models, prompt engineering is a technique that involves crafting specific input queries or instructions to guide the model, better tailoring the outputs or responses. With RAG, we can augment prompts with proprietary data, equipping the AI model to generate relevant and accurate responses with that enterprise data taken into account. This approach is also preferable to the time-consuming and resource-intensive approach of re-training or fine-tuning an LLM with this data.
Challenges and considerations
Of course, working with generative AI is not without its challenges. If your organization is looking to harness GenAI’s potential, you should bear in mind the following key issues.
Need for data expertise and massive compute power
Generative models demand substantial resources. First, you need the expertise of trained data scientists and engineers. With the exception of data organizations, most enterprises don’t have teams with the specialized skillset that would be needed to train or fine-tune LLMs
When it comes to computing resources, training a model on comprehensive data may require weeks or months—and this is even if you’re using powerful GPUs or TPUs. And although fine-tuning an LLM may not require as much computing power as training one from scratch, it still requires significant resources.
The resource-intensive training and fine-tuning of an LLM is what makes RAG an attractive alternative technique for incorporating current (and proprietary) data with the existing data available to a pre-trained LLM.
Ethical considerations
The rise of generative AI has also spawned intense discussion over the ethical considerations that come with its development and use. As generative AI applications become more mainstream and accessible to the public, conversations have centered around how to:
- Ensure equitable and bias-free models
- Protect against attacks like model poisoning or model tampering
- Prevent the spread of disinformation
- Guard against the misuse of generative AI (think deepfakes or generating misleading information)
- Preserve attribution
- Promote transparency with end users, so that they know when they’re interacting with a generative AI chatbot rather than a human
The hype and novelty of generative AI tools have eclipsed the broader AI landscape of tools and systems. Many mistakenly assume that generative AI is the AI tool to solve all their problems. However, while generative AI excels in creating new content, other AI tools might be better suited for certain business tasks. The benefits of generative AI should—just as with any tool in your stack—be weighed against the benefits of other tools.
RAG-specific challenges
The RAG approach to leveraging a large language model is powerful, but it comes with its own set of challenges as well.
- Choosing vector database and search technologies: Ultimately, the efficiency of the RAG approach hinges on its ability to retrieve relevant data quickly. This makes the selection of a vector database and search technology a critical decision that will affect RAG performance.
- Data consistency: Because RAG pulls data in real time, ensuring that the vector database is up-to-date and consistent is essential.
- Integration complexity: Integrating RAG with an LLM adds a layer of complexity to your systems. Effectively implementing generative AI with RAG may require specialized expertise.
These challenges notwithstanding, RAG affords organizations with a straightforward and powerful means of tapping into their operational and application data to glean rich insights and inform critical business decisions.
MongoDB Atlas for GenAI-powered apps
We’ve touched on the transformative potential of generative AI, and we’ve seen the powerful enhancement of real-time data that comes with RAG. Bringing these technologies together requires a flexible data platform that offers a suite of features tailored for GenAI-powered applications. For organizations venturing into the world of generative AI and RAG, MongoDB Atlas will be the game-changer.
The core features of MongoDB Atlas include:
- Native vector search capabilities: Native vector storage and search are built into MongoDB Atlas, ensuring quick and efficient data retrieval for RAG without the need for an additional database to handle vectors.
- Unified API and flexible document model: The unified API from MongoDB Atlas allows developers to combine vector search with other query capabilities, like structured search or text search. This, coupled with MongoDB’s document data model, brings incredible flexibility to your implementation.
- Scalability, reliability, and security: MongoDB Atlas provides horizontal scaling to easily grow as you (and your data) grow. With fault tolerance and simple horizontal and vertical scaling, MongoDB Atlas ensures uninterrupted service regardless of your workload demands. And, of course, MongoDB shows how it prioritizes security by enabling industry-leading data encryption that is queryable.
