What are AI hallucinations?

If you’ve ever experienced a hallucinating AI system, then you know that AI, unfortunately, can’t always be trusted. It’s one of the most common problems with any AI tool: the ability to make things up. Earlier in 2024, the viral strawberry problem brought public attention to the issue of AI hallucinations.

In the example of the strawberry problem, if you ask generative AI like ChatGPT or Claude how many times the letter “r” appears in the word “strawberry,” it will tell you that it only appears twice. And then if you ask the AI if it was certain about that response, it doubles down on its answer.

This is an easy mistake to catch since the proof of the AI being wrong is right in front of you. But if you’re using AI to research a topic, or if you’re a customer reaching out with specific questions about a business, you might not realize the provided information is incorrect.

Given these errors, you might be hesitant to rely on large language models to communicate information to your customers. The good news is that you can still leverage the power of AI by creating your own library of information using SignalWire Datasphere to prevent generative AI from making things up.

What is SignalWire Datasphere?

SignalWire Datasphere is a Retrieval-Augmented Generation (RAG) API that handles vast data sources for real-time AI communication systems. By enabling rapid data retrieval from structured documents like PDFs, Datasphere allows you to create your own library of information instead of pulling from the sources that train models like ChatGPT.

Unstructured data is transformed into searchable information, allowing developers to build more responsive, intelligent AI agents that can deliver precise insights to customers pulled from a dynamic knowledge base.

Whether you’re building a virtual assistant, automating customer service inquiries, or integrating AI into your company’s communication workflows, Datasphere enables the AI to access accurate and specific information to create a more reliable AI system for your business.

How does Datasphere work?

Datasphere enhances AI communication by improving how systems retrieve and use data. Document data is transformed into vectors for AI systems to process efficiently for smarter, faster, and more relevant real-time communication.

The API allows for the ingestion of documents like PDFs, breaking them into manageable chunks to be stored and searched later. This makes it an invaluable tool for building real-time communication applications that rely on data-intensive processes like automated customer service or AI voice assistants.

Breaking down information for AI

Vectorization is the process of transforming text into data vectors that AI can process efficiently. Datasphere does this by breaking documents into “chunks,” manageable pieces of text that the system can analyze, index, and retrieve quickly for easier processing by the AI.

By chunking documents, developers can ensure their AI systems retrieve information more accurately and quickly, allowing them to provide immediate responses based on large data sets.

Different chunking strategies allow for flexibility based on the document structure, such as splitting by sentence, paragraph, or page. Choosing the right chunking method significantly impacts how effectively the AI retrieves and interprets data.

Sentence-based chunking breaks content at sentence boundaries so the natural flow of language is preserved and is best for narrative content like instructional guides.

Sliding window chunking splits content into overlapping word chunks, maintaining continuity between sections. This preserves context and ensures AI retrieves results without losing key details. This is ideal for long, complex documents like research papers or legal text where context extends across sections.

Paragraph and page chunking use natural structures like paragraphs and pages to split content. This strategy works well for text with headings or defined sections like articles or FAQs.

Data retrieval for intelligent responses

Once documents are vectorized, Datasphere allows you to perform advanced searches within the data. Whether you need to search across entire databases or within specific documents, Datasphere's search capabilities can be customized to retrieve the most relevant information for any query.

For example, you can refine search queries with filters like tags or proximity, ensuring that the AI retrieves only the most relevant and accurate data.

Full control over your data

Managing large volumes of data is often one of the biggest challenges for AI systems. Datasphere not only allows you to upload and vectorize documents, but also gives you complete control over them. You can list, update, or delete documents as needed, ensuring your data stays up-to-date and relevant.

This makes it easy for businesses to manage growing datasets, allowing AI systems to remain efficient and accurate with changing information.

Additionally, using Datasphere improves safety and privacy for AI applications. Since you control the data and the applications, no personally identifiable data (usernames, passwords, credit card numbers, etc.) is shared with third parties, improving security.

Datasphere use cases

Datasphere ensures that generative AI applications perform at their best for contextually relevant and correct responses. With its ability to process and retrieve information in real time, users benefit from quick, accurate results that enhance customer interactions and internal workflows.

Here are just a few ways SignalWire Datasphere can transform business processes:

• Datasphere powers intelligent virtual assistants by allowing them to retrieve relevant information quickly. Whether customers are asking for specific product details or troubleshooting a problem, the assistant can pull accurate data in real time, providing faster and more personalized service.

• In high-volume environments like contact centers, AI solutions need to handle massive amounts of customer interactions efficiently. Datasphere helps streamline this by offering advanced data retrieval that enhances the accuracy and speed of AI responses.

• For educational platforms using AI, Datasphere enables real-time access to ever-changing course materials or research documents. It can be used to answer questions, provide additional reading materials, or support dynamic learning environments with instant data access.

Start building with SignalWire’s Datasphere API

SignalWire’s Datasphere API is an advanced solution for businesses looking to maximize the full potential of AI communication. With its ability to vectorize, manage, and retrieve data efficiently, Datasphere empowers developers to build smarter, faster applications that prevent hallucinations and other inaccuracies.

If your goal is to make your AI systems more intelligent, start building for free today with SignalWire Datasphere. Sign up for a SignalWire Space, and join our developer community on Slack, Discord or our community forum!