What is RAG (Retrieval Augmented Generation)? How to Expand Your Custom AI App Beyond Its Training Data
RAG (Retrieval Augmented Generation) enables LLMs to access a knowledge base outside of its training data sources before generating a response. RAG is quickly becoming the leading solution for AI developers or custom GPT builders to expand the scope of their application and
- Large Language Models (LLMs): LLMs are a powerful type of artificial intelligence (AI) that are trained on vast datasets of text and code. Being trained on vast amounts of data allows the LLM to notice statistical patterns in language, making predictions in response to your queries that enable them to perform remarkable feats like generating human-quality content, translating languages, answering user questions, and more.
- Generative Pre-trained Transformers (GPTs): GPTs are a specific type of LLM architecture, used in some of the leading models. They excel at tasks like text generation and completion, making them a popular choice for building custom AI applications.
RAG (Retrieval Augmented Generation) enables LLMs to access a knowledge base outside of its training data sources before generating a response. RAG integrates an external data source — a RAG API — with your LLM, allowing it to ingest real-time information. This goes beyond the limitations of the model’s original training data, significantly expanding its capabilities.
This creates a powerful conversational interface where users can interact with their data in a natural way, generating deeper insights much faster. By properly fine-tuning your LLM with the right proprietary data, RAG can reduce hallucinations and produce better more accurate queries.
How Does RAG Elevate Your Custom AI App?
Current custom AI applications rely solely on the data they’re trained on. This data is often isolated, static, and, by its very nature, outdated. RAG bridges this gap by connecting your AI app to dynamic online sources. This real-time data infusion unlocks a range of improvements:
- Enhanced Chatbot Responses: Imagine a chatbot that can not only answer questions based on its training, but can also perform real-time web searches, access breaking news, or provide up-to-date financial information and weather forecasts.
- Expanded App Capabilities: RAG empowers your AI app to go beyond its initial programming. It can now access and leverage external data sources, allowing it to perform new tasks and cater to a wider range of user needs.
The Dappier Advantage
Dappier, a secure multi-tenant AI platform, empowers data-driven businesses and AI developers alike to leverage RAG’s potential. We harness proprietary real time data updates from trusted brands and fine-tune it to create an AI agent that’s an expert in your brand. Open doors to new revenue streams and bridge the data gap, granting your users access to real-time information while ensuring a secure and scalable platform.
RAG unlocks a new level of intelligence and adaptability for your custom AI applications. Dappier makes available real-time data sets that you can integrate into your LLM driven applications with a simple to use RAG API, so that your application can generate better responses and access useful proprietary data.
To try Dappier Real Time Data Model and integrate our RAG API for free into your application and unlock real-time data updates for your GPT, visit platform.dappier.com.
