How LLMs Are Transforming Enterprise Applications?

Offering personalized context and services on an app was a far-fetched dream. Now all you need is the right LLM, a few lines of code and the relevant database.

Artificial Intelligence is bringing significant transformation in businesses across the world. With generative AI solutions, businesses are able to reach high levels of efficiency, automation and productivity. 

AI solutions are not only helping businesses modernize their internal processes and models but also offering quality personalized services to customers through integrated AI systems in their offerings.

One of the ways businesses are employing generative AI in their products is through LLM based apps. Generative AI interfaces trained on relevant data that can offer personalized context to users are the future of mobile apps.

Businesses have realized the power of LLMs. They are now fine tuning LLM models with domain specific databases to align them with their services and offer precisely personalized services that were not possible until recently. This is what makes apps built on LLMs powerful. 

Most importantly these apps powered by LLMs are now AI agents. These AI agents when trained with the right database can do much more than just answering customer queries.

It may sound complicated but now anyone with the right database and an API can develop such experiences. You can either use an API like ChatGPT API or an open source LLM like LLaMa 2. 

Which is Cheaper to deploy Open Source LLMs Vs OpenAI GPT models? Read Here

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How Generative AI is transforming Apps?

Most of you will associate AI agents with smart chatbots that you see on websites. However, AI agents can do much more than just answering your queries and pull out a few FAQs.

AI agents or copilots are not just mere chatbots. These are new age smart digital assistants who are trained to do specific tasks as commanded by the users. 

For example, Microsoft 365 Copilot also known as Copilot can help you:

1. Draft documents
2. Summarize pdfs
3. Manage and respond to emails
4. Help you make presentations
5. Plan meetings and take meeting notes
6. Make ppts faster

…and more.

You can create AI agents or AI apps for your specific business, industry and service. All you need to do is to fine tune LLM with the right context and boost its memory via access to a reliable, relevant and scalable database. 

If you are in the finance sector, you can build an AI agent app that can monitor, track and manage personal finances. It can also give personalized financial tips to individuals based on their portfolios. 

For example, Cleo, a chatbot provides budgeting assistance by linking directly to the user's bank account.

Another example is Fyle which is an employee expense management app. It can handle expense reporting, real-time data extraction, travel requests and advances, analytics and much more.

For the Edtech sector, AI agents can be trained with specific curriculum databases. Thus the AI app can help students understand concepts, refine their knowledge and even practice lessons. 

For example, ELSA Speak helps people perfect their English speaking skills. The app uses speech data and voice recognition technology.

You can too build an AI app for your business. Our generative AI services will help you build smart AI apps and AI agent solutions aligned with your business goals and customer preferences.

AI apps with generative AI capabilities have the potential to:

1. upgrade your services
2. boost customer experience
3. increase sales
4. offer personalized services
5. the surge in customer retention rate
6. offer unique services aligned to your customer's personal preferences

Apps built on LLMs function like super apps that leverage the power of vast data to carry out several tasks simultaneously that were not possible before.

Let us understand with an example:

Suppose you want to build a beautiful garden in your backyard. Now to buy equipment and garden décor items you go to a gardening app. 

As this app is powered by LLMs and has access to data sources that contain company inventory, product catalog, order history, customer preferences and other data, the app can come up with a personalized list of items for the gardening project.

That’s not all! As the app has access to order history, the AI model can analyze the customer's past orders learn about his décor aesthetics and offer items that match his taste. 

There's more. If the customer enters the dimension of his backyard and details how he wants the garden, the app can also come up with a virtual design. With AR/VR technology the customer can visualize the virtual design in his backyard before starting his gardening project. 

Also, the app has your address and postal code. So, the AI agent can also suggest contractors for the project near you. The app can also show you near by brand stores to explore and buy items. 

Moreover, the app can also tell the time and other supplies required for this gardening project. 

Therefore, here the AI agent app acts like your assistant helping you at every step of your project.

Achieving this level of personalization with mobile apps was not possible before LLMs.

4 Ways Enterprises Are Building LLM Based Apps

LLMs are not that complex to work with. Moreover, if you are using an API like GPT-4, your work gets even simpler. 

There are 4 common ways enterprises are employing LLMs in production.

Prompt Engineering with Context - This lightweight approach involves calling third-party LLM APIs (e.g. OpenAI, Anthropic, Cohere) with a carefully crafted prompt and sample inputs as context. It's an easy way to get started with LLMs.

Retrieval Augmented Generation (RAG) - For domain-specific use cases without sufficient fine-tuning data, RAG retrieves relevant data snippets to provide context to prompts. Emerging tools like Pinecone and LlamaIndex enable efficient retrieval.

Fine-Tuned Model - With larger datasets and business-critical applications, fine-tuning trains an LLM on an enterprise's data for higher response quality and lower costs. Services like Databricks & Vertex AI simplify fine-tuning workflows.

Trained Model - Building an LLM from scratch by training on massive proprietary datasets. This approach is resource-intensive but yields the highest quality for specialized domains.

If you are looking to build an app with open source LLMs or OpenAI GPT Models, we are here to help. Get started on your AI agent with our Generative AI solutions.

Using LLMs In Your App With the Right Context, Memory And Database

Here we will talk about the prompt engineering approach to build LLM based apps.

Using LLMs APIs like OpenAI and Anthropic is remarkably straightforward. 

They operate by taking a context, often referred to as a "prompt," and generating a corresponding response. Therefore, the process of creating an agent begins with devising a strategy to offer the appropriate context to the LLM to elicit the desired reply.

In essence, this context originates from three main sources: the user's inquiry, predefined prompts crafted by the agent's developer, and data extracted from databases or alternative origins. 

The context provided by the user generally entails the question they enter into the application. The second aspect can be supplied by a product manager collaborating with a developer to outline the agent's intended role (such as, "You're a helpful sales agent aiding customers in project planning; please include a list of relevant products in your answers").

Lastly, the third pool of context comprises external data fetched from databases and other data reservoirs that the LLM should employ to formulate the response. 

In certain agent applications, multiple interactions with the LLM might occur before delivering the user's response, enabling the creation of more intricate and comprehensive replies.

AI agents rely on a source of information, but this information needs to be understandable for a Large Language Model (LLM). 

Let's pause for a moment to consider the functioning of LLMs. 

When you pose a question to ChatGPT, it operates within a limited memory or "context window." During extended conversations, ChatGPT compiles your previous queries and responses and presents this to the model, but the context gradually diminishes.

This underscores the significance of connecting an agent to a database for companies aiming to construct applications centred around LLMs. However, the database must organize information in a manner comprehensible to an LLM—using vectors.

To simplify, vectors allow the compression of a sentence, concept, or image into a set of dimensions. A concept or context, like a product description, can be translated into multiple dimensions: a representation in the form of a vector. 

Storing these dimensions facilitates vector search—an ability to explore multi-dimensional concepts instead of relying solely on keywords.

This approach assists LLMs in generating more precise and contextually fitting responses, while also serving as a form of enduring memory for the models. 

Essentially, vector search establishes a crucial link between LLMs and the extensive knowledge repositories that underpin their training. Vectors function as the "language" of LLMs, and vector search stands as a necessary feature of databases providing contextual information.

Consequently, a pivotal element in supplying LLMs with suitable data involves a vector database with the capacity, scalability, and dependability necessary to manage the immense datasets required for powering agent experiences.

Selecting The Right Database 

Scalability and performance are two critical considerations when selecting a database for AI/ML applications. 

Agents demand access to substantial real-time data and necessitate rapid processing, particularly when deploying agents intended for use by every visitor to your website or user of your mobile application. 

The ability to swiftly scale in response to demand is crucial for effectively storing the data that fuels agent-based applications.

Apache Cassandra, a database relied upon by industry leaders such as Netflix, Uber, and FedEx, forms the backbone of their engagement systems. As businesses increasingly embrace AI-driven interactions, Cassandra's horizontal scalability, speed, and unwavering stability become indispensable for supporting agent applications.

For this reason, the Cassandra community has developed essential vector search capabilities to simplify the task of constructing AI applications using extensive datasets. 

DataStax has made these capabilities conveniently available through the cloud with Astra DB, the pioneering petascale NoSQL database designed with AI-readiness and vector capabilities.

Conclusion

With the rise of LLMs, all apps will become AI apps and agents. If you are a business, and still wondering, it's the right time to upgrade your traditional app to an AI app and elevate your services.

Get in touch with AI experts at Ampcome for your project requirements.