How LLMs Are Transforming Enterprise Applications

Artificial intelligence is the most transformative paradigm shift since the internet took hold in 1994. And it’s got a lot of corporations, understandably, scrambling to infuse AI into the way they do business.

One of the most important ways this is happening is via generative AI and large language models (LLMs), and it’s far beyond asking ChatGPT to write a post about a particular topic for a corporate blog or even to help write code. In fact, LLMs are rapidly becoming an integral part of the application stack.

Building generative AI interfaces like ChatGPT — “agents” — atop a database that contains all the data necessary and that can “speak the language” of LLMs is the future (and, increasingly, the present) of mobile apps. The level of dynamic interaction, access to vast amounts of public and proprietary data, and ability to adapt to specific situations make applications built on LLMs powerful and engaging in a way that’s not been available until recently.

And the technology has quickly evolved to the extent that virtually anyone with the right database and the right APIs can build these experiences. Let’s look at what’s involved.

Generative AI Revolutionizes the Way Applications Work

When some people hear “agent” and “AI” in the same sentence, they think about the simple chatbot that appears as a pop-up window that asks how it can help when they visit an e-commerce site. But LLMs can do much more than respond with simple conversational prompts and answers pulled from an FAQ. When they have access to the right data, applications built on LLMs can drive far more advanced ways to interact with us that deliver expertly curated information that is more useful, specific, rich — and often uncannily prescient.

Here’s an example:

You want to build a deck in your backyard, so you open your home-improvement store’s mobile application and ask it to build you a shopping list. Because the application is connected to an LLM like GPT-4 and many data sources (the company’s own product catalog, store inventory, customer information and order history, along with a host of other data sources), it can easily tell you what you’ll need to complete your DIY project. But it can do much more.

If you describe the dimensions and features you want to include in your deck, the application can offer visualization tools and design aids. Because it knows your postal ZIP code, it can tell you which stores within your vicinity have the items you need in stock. It can also, based on the data in your purchase history, suggest that you might need a contractor to help you with the job — and provide contact information for professionals near you.

The application could also tell you the amount of time it will take deck stain to dry (even including the seasonal climate trends for where you live) and how long it’ll be until you can actually have that birthday party on your deck that you’ve been planning. The application could also assist with and provide information on a host of other related areas, including details on project permit requirements and the effect of the construction on your property value. Have more questions? The application can help you at every step of the way as a helpful assistant that gets you where you want to go.

Using LLMs in Your Application Is Hard, Right?

This isn’t science fiction. Many organizations, including some of the largest DataStax customers, are working on many projects that incorporate generative AI.

But these projects aren’t just the realm of big, established enterprises; they don’t require vast knowledge about machine learning or data science or ML model training. In fact, building LLM-based applications requires little more than a developer who can make a database call and an API call. Building applications that can provide levels of personalized context that were unheard of until recently is a reality that can be realized with anyone who has the right database, a few lines of code and an LLM like GPT-4.

LLMs are very simple to use. They take context (often referred to as a “prompt”) and produce a response. So, building an agent starts with thinking about how to provide the right context to the LLM to get the desired response.

Broadly speaking, this context comes from three places: the user’s question, the predefined prompts created by the agent’s developer and data sourced from a database or other sources (see the diagram below).

The context provided by the user is typically simply the question they input into the application. The second piece could be provided by a product manager who worked with a developer to describe the role the agent should play (for example, “you’re a helpful sales agent who is trying to help customers as they plan their projects; please include a list of relevant products in your responses”).

Finally, the third bucket of provided context includes external data pulled from your databases and other data sources that the LLM should use to construct the response. Some agent applications may make several calls to the LLM before outputting the response to the user in order to construct more detailed responses. This is what technologies such as ChatGPT Plug-ins and LangChain facilitate (more on these below).

Giving LLMs Memory

AI agents need a source of knowledge, but that knowledge has to be understandable by an LLM. Let’s take a quick step back and think about how LLMs work. When you ask ChatGPT a question, it has very limited memory or “context window.” If you’re having an extended conversation with ChatGPT, it packs up your previous queries and the corresponding responses and sends that back to the model, but it starts to “forget” the context.

This is why connecting an agent to a database is so important to companies that want to build agent-based applications on top of LLMs. But the database has to store information in a way that an LLM understands: as vectors.

Simply put, vectors enable you to reduce a sentence, concept or image into a set of dimensions. You can take a concept or context, such as a product description, and turn it into several dimensions: a representation of a vector. Recording those dimensions enables vector search: the ability to search on multidimensional concepts, rather than keywords.

This helps LLMs generate more accurate and contextually appropriate responses while also providing a form of long-term memory for the models. In essence, vector search is a vital bridge between LLMs and the vast knowledge bases on which they are trained. Vectors are the “language” of LLMs; vector search is a required capability of databases that provide them with context.

Consequently, a key component of being able to serve LLMs with the appropriate data is a vector database that has the throughput, scalability and reliability to handle the massive datasets required to fuel agent experiences.

… with the Right Database

Scalability and performance are two critical factors to consider when choosing a database for any AI/ML applications. Agents require access to vast amounts of real-time data and require high-speed processing, especially when deploying agents that might be used by every customer who visits your website or uses your mobile application. The ability to scale quickly when needed is paramount to success when it comes to storing data that feeds agent applications.

Apache Cassandra is a database that’s relied on by leaders like Netflix, Uber and FedEx to drive their systems of engagement, and AI has become essential to enriching every interaction that a business serves. As engagement becomes agent-powered, Cassandra becomes essential by providing the horizontal scalability, speed and rock-solid stability that makes it a natural choice for storing the data required to power agent-based applications.

For this reason, the Cassandra community developed the critical vector search capabilities to simplify the task of building AI applications on huge datasets, and DataStax has made these capabilities easily consumable via the cloud in Astra DB, the first petascale NoSQL database that is AI-ready with vector capabilities (read more about this news here).

How It Is Being Done

There are a few routes for organizations to create agent application experiences, as we alluded to earlier. You’ll hear developers talk about frameworks like LangChain, which as the name implies, enables the development of LLM-powered agents by chaining together the inputs and outputs of multiple LLM invocations and automatically pulling in the right data from the right data sources as needed.

But the most important way to move forward with building these kinds of experiences is to tap into the most popular agent on the globe right now: ChatGPT.

ChatGPT plugins enable third-party organizations to connect to ChatGPT with add-ons that serve up information on those companies. Think about Facebook. It became the social network platform, with a huge ecosystem of organizations building games, content and news feeds that could plug into it. ChatGPT has become that kind of platform: a “super agent.”

Your developers might be working on building your organization’s own proprietary agent-based application experience using a framework like LangChain, but focusing solely on that will come with a huge opportunity cost. If they aren’t working on a ChatGPT plugin, your organization will miss out on a massive distribution opportunity to integrate context that is specific to your business into the range of possible information ChatGPT can supply or actions it can recommend to its users.

A range of companies, including Instacart, Expedia, OpenTable and Slack have built ChatGPT plugins already; think about the competitive edge their integration with ChatGPT might create.

An Accessible Agent for Change

Building ChatGPT plug-ins will be a critical part of the AI-agent projects that businesses will look to engage in. Having the right data architecture — in particular, a vector database — makes it substantially easier to build very high-performance agent experiences that can quickly retrieve the right information to power those responses.

All applications will become AI applications. The rise of LLMs and capabilities like ChatGPT plugins is making this future much more accessible.

Want to learn more about vector search in Cassandra? Register for the June 15 webinar.