Generative AI and Vector Databases: The Future of Data Retrieval
In an era where data is growing at an exponential pace, traditional approaches to data retrieval are reaching their limits. Text-based search engines and relational databases, while still foundational, often struggle to meet the complex demands of modern applications. Enter generative AI and vector databases—two technologies that, when combined, promise to revolutionize the way we store, retrieve, and interact with data. At the heart of this transformation lies a key realization: modern data isn’t just numbers and strings; it’s unstructured, contextual, and deeply nuanced. Text, images, videos, and even voice recordings contain layers of meaning that can’t always be captured through keyword matching or rigid schemas.
The speed and precision of data retrieval are crucial for informed decision-making. The growing convergence of generative AI and vector databases is transforming how we access and leverage information, providing groundbreaking solutions for both businesses and individuals. Generative AI and the use of vector databases are steering the future of data retrieval in a more innovative and dynamic direction, including the enormous possibilities of querying the data using natural language without any programming skills. Those people who understand the business are able to ask questions from the data without first asking an IT person to write a piece of code.
Generative AI
Generative AI is a class of machine learning models designed to create new content, such as text, images, music, or, for example, code. Generative AI learns from large datasets during training, identifying patterns, and then, using these patterns, generates novel content that is contextually relevant, coherent, and often indistinguishable from human-created works.
A key element of Generative AI is a foundation model. A foundation model is a pre-trained machine learning model that serves as a base for a wide variety of downstream tasks. These models are trained with huge amounts of money, time, resources, and special knowledge. They are trained on massive datasets covering diverse types of content - good and bad. A foundation model can be, for example, a language model to perform tasks like text generation, summarization, or translation, or a vision model for image classification, object detection, or even multimodal tasks combining text and images. Multimodal models combine multiple data types (text, image, audio) for creative or analytical tasks and can, for example, create videos or podcasts based on instructions given using natural language, spoken or written.
Two major problems with foundation models are old data and hallucination. Since model training is a time-consuming task, models can only be trained rarely. Therefore, their knowledge is based on old data—data from the time of training—unless they are able to use online data sources. Hallucination is a feature of Generative AI, and it means that if the model does not have the answer, it invents it. There are some methods for reducing hallucination, but unfortunately, it is not possible to avoid hallucination 100%.
Fine-tuning is used for training a foundation model for a specific domain or tasks/skills. The data used for fine-tuning does not need to be large, but it needs to be of good quality. Fine-tuning can be used to introduce new data to the model with less time and resources than training a foundation model. It can also be used to reduce hallucination since it will have more information about the task in scope.
A model is called using a prompt. A prompt defines what the model is expected to do: write a summary, answer a question, draw a picture, create a video, or whatever the imagination of the person prompting allows. Prompt engineering is the number one tool for a user when using Generative AI. Prompt engineering is a set of methods and techniques for creating better prompts to guide the model to perform better. The prompt consists, at least, of a system prompt and a user prompt. System prompts are a set of instructions, guidelines, and contextual information provided to models before they engage with user queries. Different techniques for user queries are, for example, in-context learning, k-shot prompting, or Chain-of-Thought. The better the prompt sent to the model is, the better responses the model is able to deliver.
Vector Databases
Vector databases are designed to handle high-dimensional data, particularly vectors that represent data in a multi-dimensional space. Vector databases store data in the form of vectors that represent complex data from words and sentences to images and sounds. This complex data can be converted into numerical vectors using embedding models. Vectors capture the semantic meaning of the data, allowing computers to understand the relationships between the data. In a vector database, vectors can be indexed and retrieved efficiently based on similarity, rather than an exact match. For example, a keyword search for example, “city = ‘STOKCHOLM’,” would not return anything because the city is called Stockholm, and it is stored in the database as capitalized, not as all letters uppercase. A similarity search would find it with, for example, a prompt “Cities in Sweden?”. Similarity search understands the context and the meaning of the data, not just how it’s spelled. A vector database enables efficient retrieval of semantically similar data by storing vectors and using advanced similarity search techniques, making it an essential tool for AI-driven applications like recommendation engines, image and text similarity search, outlier detection, and more.
If a vector database happens to be a multi-model database, like the Oracle Database 23ai, you can combine the similarity search powered by vectors with other datatypes in the database, such as relational business data, spatial data, or graphs. When these technologies converge, they create a powerful framework for enhanced data retrieval.
Combining Generative AI and a Vector Database
While both Generative AI and vector databases are powerful on their own, their integration has the potential to revolutionize data retrieval in profound ways.
As mentioned earlier, Generative AI is typically engaged through a prompt. A prompt is a question asked by a user using natural language, for example, “What is the capital of Finland?” The prompt is sent to the AI model to respond. Depending on the prompt, the model, and the data it has been trained with, the response can be useful, completely invented, hallucinated.
The prompt can be vectorized using an embedding model (the same model that was used when the data was stored in the vector database), and compared to the vectors in a vector database for a similarity search. Those vectors that are similar enough are returned and used to augment the prompt before it is given to the AI model to respond. This process is called Retrieval-Augmented Generation (RAG). RAG makes it possible to add your own data to the process without training a model. It is also a method to reduce hallucination. And, if the retriever model does not find anything related to the question from your data, it can be programmed to say “I do not know”. A RAG solution also enables grounding. Grounding means that the AI model identifies where the answer was found and the user can go and verify it.
The Future of Data Retrieval: Opportunities and Challenges
The potential applications of Generative AI and vector databases are vast. They can be used in every line of business: healthcare, quality control, education, and customer services, just to mention some. It enables tasks that were not able to be done before and boosts many existing tasks. But when using Generative AI and vector databases it is important to understand what you are doing. It is not a silver bullet that will solve all the issues. If the data quality is bad, there is no method or technique that would hide that. The more the data is used for decision making the bigger the problem with bad data is. Bad data quality will result in wrong decisions.
Also, it is important to understand that hallucination is part of the AI model’s “job” when creating new content. Even though hallucination can be reduced, it cannot be avoided 100%. It is important to implement guarding systems to detect hallucination. The easiest guard is a human being verifying the outcome before it is published.
Other concerns are legal issues such as privacy, security, intellectual property rights (IPR), acts, laws, and so on. Also, ethical issues such as bias and misinformation must be taken into account when using new technologies. It is often forgotten that people need to be trained. The way these new technologies operate is very different from the ones they have been using. It is important that they understand the differences and are able to use them correctly. As for any technology there are always security risks. Examples of these are jailbreaks, prompt injections, data poisoning, or backdoor attacks.
The change has been fast—ChatGPT was only introduced on November 30th, 2022—but we can be sure it is here to stay. Generative AI and vector databases have changed our lives and will continue to do so.
Conclusion
Generative AI and vector databases represent the cutting edge of data retrieval. By combining the creative capabilities of AI with the high-dimensional, semantically rich structure of vector databases, we can look forward to a future where data retrieval is more intuitive, efficient, and personalized than ever before.
As these technologies continue to mature, businesses and developers will be at the forefront of a new era of data interaction—one that is smarter, faster, and far more powerful than traditional approaches. The future of data retrieval is not just about finding information; it’s about understanding it in deeper, more meaningful ways. And with generative AI and vector databases leading the charge, that future is just beginning to unfold.
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