What is Generative AI?

What is Generative AI?
Learn about generative AI and advanced machine learning. Explore its impact and future potential in our latest blog post.

Unlike traditional AI models that are primarily designed for analysis and decision-making, generative AI has a more creative focus. It concentrates on creating new content, ranging from text to images, or even songs. Due to the number of new use cases it could be applied to, it has started a paradigm shift in the technology world.

In this blog post, we will explore the different aspects of generative AI and its significance.

Defining Generative AI

Generative AI is a subset of artificial intelligence that specializes in generating new content. It stands apart from traditional AI models that are often limited to processing and interpreting data only. At its core, generative AI uses machine learning algorithms to produce new data similar to the data it was trained on. 

The essence of generative AI lies in its ability to learn from a set of data and then use this learning to generate new, original content. This is achieved through sophisticated machine learning models such as neural networks. These networks are then trained on large datasets, allowing them to capture intricate patterns and relationships within the data. 

Once trained, the models can draw from what they have learned to produce new data examples. But when it comes to Generative AI, it's important to measure the predictive power of your data before actually building a solution. 

This is where the proven framework we use comes into play, a strategy that shortens time to market and has been tested by both startups and billion-dollar companies.

Large Language Models (LLMs) And Your Business DNA

At the heart of our approach to generative AI is the belief that LLMs should echo your data, brand, and core beliefs. For example, our case study on Retrieval Augmented Generation for enterprise AI projects shows how AI can improve data retrieval and processing, powering more sophisticated AI solutions. At NineTwoThree Studio we ensure this is possible through the following:

Fine-Tuning for Efficiency and Accuracy

Fine-tuning allows the use of smaller, less expensive models to enhance the accuracy of an existing model. This process is not just about cost-saving; it's about optimizing performance to meet your requirements.

Prompt Engineering: Guiding AI for Brand Coherence

Prompts serve as a navigational tool, steering the model in a direction that benefits the brand. This technique helps control costs, increase accuracy, and improve response times.

Deploying Agents for Enhanced Interaction

AI requires tools for tasks such as querying current weather conditions or searching through a CRM database. We design and deploy agents that enable LLMs to perform these tasks on command, improving the accuracy of responses.

Implementing Guardrails: Keeping Conversations on Track

While LLMs can go into a wide range of topics, not all are helpful. Our strategy involves implementing guardrails to steer conversations back toward the objectives, ensuring that interactions remain relevant.

Building a Customized Knowledge Base

Basing responses on your own data is critical for accuracy. We assist in developing a knowledge base that provides the model with information applicable to your business.

Memory Management for Efficient Customer Service

Repeated queries from customers are a common challenge. Our approach includes managing similar responses and reducing response times without additional costs.

Beyond the Basics: AI as Your Business Co-Pilot

Our vision extends beyond conventional applications of AI. From responding to social media comments with on-brand messaging to automating routine tasks, the potential applications are endless.

Key Technologies and Mechanisms

When deploying generative AI projects managing information is ultimately the most important element. Key technologies have been developed to turn raw data into a structured knowledge base that AI can interpret, analyze, and act on. 

When we add information to our knowledge system, we change it from words to a computer-friendly format. We do this by making an "embedding," which is like turning the idea into a series of numbers that a machine can read. 

After we save these number series, the system can figure out how these ideas are connected through its Vector embeddings, or just vectors. A vector turns information into a set of numbers that relate to each other. For languages, it means capturing what words mean and how they're used together.

Unfortunately, not all data is immediately compatible with AI's processing capabilities, especially when dealing with lengthy or unstructured documents. Tools like Langchain's Document Loader help contextualization of a wide collection of documents - from videos and PDFs to spreadsheets and FAQs. These documents are then broken down into manageable chunks that keep their original meaning.

Beyond data management, the application of semantic search shows AI's ability to learn nuanced human communication. When talking to other humans, we often rely on the context and not just the words. Like when you describe something at the store that's red like a strawberry but similar to a blueberry, you might be talking about a raspberry. You didn't use the exact word, but the meaning was clear. 

Semantic search allows computers to make these kinds of connections, making our interactions with them feel more like talking to another person.

Turning Semantic Search Into Vector Relationships

We've learned that we can turn information into a computer-friendly format and store it safely in a cloud-based vector database. Now, to get natural language responses, we need to securely send our questions to a large language model (LLM).

Here's how it works:

We combine the stored information with a large language model. This combination allows for AI-powered conversations that go beyond what we've seen before. The system uses complex math to link information, making it possible to chat with the stored knowledge as if it were a person. This system knows about 40,000 English words, making it a very capable communication partner.

The process involves converting private documents into numbers (or floats) that represent their content. These numbers help the system understand the document's relevance to our question. We send these numbers, along with any other needed details from our stored knowledge, to the LLM to get our answers. OpenAI ensures our information remains private and isn't used for other purposes.

Thanks to cloud services like Google, Amazon, and Microsoft, integrating LLMs has become easier. They offer tools for businesses to securely manage their information and interact with these advanced AI systems.

So, we end up with a system that:

  1. Uses relevant, stored information for responses.
  2. Provides accurate facts without making mistakes.
  3. Cites sources for the information it uses.

Based on these key technologies and advancements, it’s fair to say that generative AI is set to go much further than we can predict in the near future. Are you ready to add Generative AI to your project? Get our AI Playbook to get started.

NineTwoThree Staff
NineTwoThree Staff
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