Exploring the foundations of AI-based search
Leveraging AI to facilitate automation and simplify our daily lives has become a central topic of conversation, fueled by the rapid adoption of generative AI in application development. One of the key areas where AI delivers substantial value is transforming our approach to search and retrieving data.
When Google launched its search engine, it revolutionized how search engines could map and present data, making it easier for people to find the information they were looking for with keyword-based queries. Searching for nearby restaurants was quick and easy, as Google mapped location data to restaurants in a 20-mile radius, offering a curated list of options. While queries could vary in scope, the core principle was simple: matching keywords to relevant data.
With the advent of AI, searching for data has fundamentally changed. When Google introduced keyword searching, it transformed how we stored and mapped data, but it still stored and presented the data in a structured way that allowed humans to decipher it. With AI, data no longer needs to be stored in a structured way; AI can store data in what is known as a vector that includes 1000s of attributes about the data, all of which AI can use to determine if that information is relevant.
The power of AI-based search provides the ability to move beyond deterministic keyword search to highly relevant non-deterministic vector search capabilities. In this guide, we will explore what AI-based search is, how companies implement it, and how applications can benefit from using AI-based search methods.
An overview of AI-based search
At the core of AI-driven search is the ability to interact with machines and algorithms, using data naturally. NLP, or natural language processing, allows machines to interact with human language without sacrificing all the complexity and diversity that language and speech provide. With NLP, computers can detect patterns and similarity of words within language and use that to provide more relevant responses.
For example, take something as simple as how we describe things in the English language. We can use the same word to mean two very different things: “My dinner was salty” means something very different compared to “Jane was acting very salty”. The word salty in these two contexts is very different. NLP provides computers with the ability to discern what is actually being stated and requested.
In addition to NLP, AI-based search leans on machine learning to provide cognitive abilities across large datasets. With machine learning, how the system reacts and responds is no longer a function of mathematics but becomes a function of recognizing patterns in data and using those patterns to make predictions and decisions. An interesting way to visualize this uses mathematics. This may seem a little counterintuitive, but it is how we were taught multiplication as children.
If we have the problem 3 * 4, we can quickly determine the answer is 12, but how did we determine that answer? Mathematically, we can represent it as 3 * 4 and as 3 + 3 + 3 + 3 or 4 + 4 + 4, but we didn't come to the answer 12 by adding 3 + 3 + 3 + 3; we just knew it was 12.
The reason for this is that, as children, many of us memorized multiplication tables so that when we saw the pattern of 3 * 4 or 4 * 3, we knew the answer was 12. Machine learning does something similar but on a much larger scale. Machine learning teaches computers to recognize patterns, memorize those patterns, and provide responses much quicker.
The combination of NLP and machine learning is transforming how search works with AI. AI now can provide more detailed, contextual responses based on natural interaction. Take our example above of searching for a restaurant near us: With traditional search, we’ll get a list of restaurants that are in the area. Then, we can search reviews and determine what might be of interest. With AI-based search, on the other hand, we can ask “What is the hottest new restaurant in the area?” and AI can provide a detailed recommendation based on location, reviews, personal context, value, and much more.
What are the benefits of using AI-based search?
Searching for information isn't new, so what is the benefit of AI-based search compared to the traditional approach we have been using? In traditional search, the correlation of data happens with the assignment of things like keywords and key/value pairs. This approach is highly scalable and well proven, but it also has limitations. The results come tightly coupled to how keywords are assigned and weighted. The results are typically generic in nature. Sure, the search engine can incorporate information like the location of the query, but advanced personalization can be difficult.
Additionally, it is extremely difficult to find relevance in large volumes of data. Just think about how people typically use Google's search engine to find information. It’s rare for a user to scroll past the first five results, and even if they do, they don’t typically scroll beyond the top 10 or 20 results. That means the user never sees hundreds, if not thousands, of results!
This is where AI-based search is changing the game for how we deal with data. AI-based search does not regulate search to a small number of keywords or key/value associations; it can use vector embeddings on data that provide thousands of different inflection points that AI can useto determine if that data is relevant to a particular query. Along with the specific information that AI uses to define the data, metadata about context can also be used by AI-based search to provide a more personalized experience. It is this ability to correlate vast amounts of information with vast amounts of context that provides the end user with a much more personalized response, ultimately giving them more relevant information and a better user experience.
Components powering AI search engines
AI-powered search engines are clearly different from traditional search engines, but how, exactly? At their core, AI-based search engines rely on two main technologies: machine learning and natural language processing (NLP). Machine learning has advanced how search engines can understand and optimize information for fast processing and retrieval. NLP, on the other hand, has changed how we interact with data by allowing systems to interpret and respond to natural, conversational language. Together, these technologies have changed how information can be stored, processed, and accessed. Both machine learning and NLP have seen significant advancements in areas like vector storage, vector search, nearest neighbor algorithms, and retrieval-augmented generation, enabling AI search engines to provide highly relevant search results efficiently.
Natural language processing (NLP)
In the early days of the internet, before Google dominated the search engine market, platforms like Yahoo!, Excite, and Ask Jeeves offered people a way to find information on the web. Ask Jeeves was a particularly promising option because it allowed people to interact with the web by asking straightforward questions like “What do I need to fix a broken drawer handle?” and then provided links to websites with the answers.
However, the problem at the time was that Ask Jeeves based its search entirely on keywords, so your question had to contain a keyword that would relate to the keyword of a website for the response. This meant that, while the engagement was more natural in the form of question and answer, the result was still very mechanical.
Fast-forward to today, and large language models (LLMs) have given rise to solutions like ChatGPT. With ChatGPT, you engage with information in a truly natural way, asking it to explain how something works or how to fix an issue. The result isn't just a list of websites with embedded information. It’s the actual explanation or step-by-step solution. Interacting with ChatGPT involves a conversation on a topic, rather than simply providing a list of reference materials. With recent announcements by OpenAI and demos showcasing how users can combine NLP with real-time audio generative AI, we now have the ability to not only interface with AI search via text, but also interact with AI in the same way we interact with people — by simply asking our AI assistant a question.
Machine learning examples
While NLP provides significant advancement in how we interact with AI-based search, machine learning is the major advancement behind the intelligence of these search engines. The key advancement in machine learning for AI-based search lies in the widespread availability of models trained on enormous datasets.
What LLMs provide is the ability to index and vectorize data in a way that was never possible before. Traditional search engines relied on keyword-based indexing, which meant that information was categorized by specific labels and keywords.. While this method provides reasonably accurate results, it is also fairly rigid. A search for dog toys, for example, will provide a list of things tagged as dog toys. However, what if you want to get your dog some tennis balls? Unless someone tags tennis balls as dog toys, they might not even come up as an option!
With AI search, machine learning, and LLMs, however, we have a different way to correlate information—semantic/vector search. With semantic search, AI-based applications have the ability to traverse massive amounts of data in real-time. Instead of looking for things tagged with the label dog toys, AI-based search can use nearest neighbor algorithms to pull all things within a close proximity to the term dog toys. More importantly, with retrieval-based augmentation (RAG), the inventory catalog updates in real time so that as people search for dog toys and purchase tennis balls, the system moves tennis balls closer and closer to the query of dog toys.
Supporting multi-modal and cross-modal searches with AI Search
AI-based search is also changing how we interact with data in the realm of multi-modal/cross-modal access. Since systems can vectorize various data types, we can now find semantic correlations across these diverse types of data. This opens up possibilities for search functionalities that weren’t possible before, since we are no longer limited to just text-based search and retrieval. With AI-based search, we can now encode data as vectors and use those vector embeddings not only to search for blocks of text but also use those encodings to search for samples of audio files, video files, and image files.
In a traditional search approach, handling diverse data types and objects is extremely challenging. For example, how would you search for an image that includes a specific logo? With AI search, images can be turned into vectors, allowing for queries based on the vector representation of the logo. The AI search engine can then use that query vector to identify and retrieve images that have similar vector embeddings, returning results that are nearest to the original logo image.
This is where AI-based search truly shines—It allows us to search across a much broader set of data. Not only can we search text, but we can also find patterns and similarities across any object that a system vectorizes.
How to build a basic AI search engine with Astra DB
Building an AI-powered search engine requires a scalable, high-performance database capable of handling vector search and real-time queries. Astra DB, a fully managed NoSQL database built on Apache Cassandra, simplifies this process with built-in vector search and AI framework integrations. Here’s a high-level guide to getting started:
- Set up Astra DB: Start by signing up for Astra DB and creating a keyspace to store your search data.
- Collect and prepare your data: Gather the data you want to index and search. This could include text documents, articles, or media files like images or audio. Astra DB efficiently stores and manages structured and semi-structured data in a distributed manner, so you can easily scale your database as your dataset grows.
- Generate embeddings for your data: To enable semantic search, convert your data into vector embeddings. Astra DB integrates with machine learning frameworks like OpenAI and Hugging Face, making it easy to generate and store embeddings.
- Index the data in Astra DB: Once you have vector embeddings, store them in Astra DB alongside the original data. Astra DB’s vector indexing enables fast similarity searches within the database.
- Set up the search API: Create an API endpoint that handles search queries by converting queries into embeddings and retrieving results using Astra DB’s built-in vector search capabilities. This enables real-time, AI-powered search.
- Build the user interface: Develop a frontend where users can enter queries and view results. Astra DB supports GraphQL and REST APIs, making it easy to integrate with web frameworks like Flask, FastAPI, React, or Vue.
- Test and optimize: Finally, fine-tune search performance by adjusting vectorization and refining search parameters. You can also leverage Astra DB’s built-in observability tools to monitor query efficiency and optimize retrieval speeds.
With these steps, you can build a powerful AI search engine while avoiding the operational complexity of traditional databases.
FAQs
1. How do you build an AI search engine from scratch?
To build an AI search engine, you start by collecting and storing your data—usually in a vector database or document store. Then, you generate embeddings using tools like Vertex AI Search, Hugging Face, or OpenAI. Those embeddings help the search engine understand a user's query using semantic search and retrieval-augmented generation (RAG). You'll also need a search bar UI, an API call layer (often a POST request), and an LLM to generate the final answer based on relevant results. Bonus: You can save time using frameworks like Langflow or libraries like LlamaIndex.
2. What’s the difference between a traditional and AI-based search engine?
Traditional search engines focus on keyword matching and ranking by exact matches. An AI search engine, however, uses neural networks, machine learning algorithms, and large language models to parse context, understand intent, and return the most relevant information—even for complex questions.
3. How does retrieval-augmented generation improve search results?
RAG combines the strengths of a search engine API and a generative AI model. It retrieves relevant data from a data store or web sources, then feeds that into an LLM to generate context-aware responses. This process helps answer the user’s question more accurately by grounding the response in real-time content.
4. How do you connect a search app to a vector database?
Use a search engine API or open-source library like LangChain to make an API call that fetches results from your vector DB. When the user submits a query, it's converted into an embedding and matched against stored vectors to find relevant results. The API returns a JSON array, which your app can parse to display documents, website links, or generated answers.
5. What tools do I need to create an AI search engine?
Start with a vector database like Astra DB or Weaviate, and a model for embedding generation like OpenAI, Hugging Face Transformers, or Vertex AI. Add a search UI (with a search bar), connect it to a backend using an API key, and use frameworks like LlamaIndex, LangChain, or Langflow to handle context window management, prompt injection, and advanced LLM features.
