Generative AI is in the process of changing the way we interact with technology. Its emergence has made the sci-fi fantasy of conversations with computers and machine-based personal assistants a reality.
But spend any time trying to build a generative AI tool and you’ll quickly run into some pretty fundamental limitations.
GenAI relies on large language models that have been trained on specific data sets. That means they won’t have access to the most up-to-date information or will provide responses that fail to completely understand the nuance or context of the question.
Retrieval-augmented generation (RAG) provides a game-changing alternative, one that allows you to construct GenAI applications based on the most relevant and recent information.
RAG enhances the accuracy of LLMs by fetching extra data beyond the dataset on which the LLM was trained.
Essentially, the LLM has excellent overall knowledge of the world. That makes it seem smart, but it’s not an expert in anything, and if you ask it specific questions, it can get stuck.
RAG allows it to become an expert, and allows you to decide what it becomes an expert in.
The term was coined in a 2020 paper on RAG which set out the technique’s applications in knowledge-intensive NLP tasks, though the principles of the technique go back several decades.
Now, RAG means powerful GenAI apps can be produced much more cost-effectively compared to other options like training your own LLM. RAG cuts operational costs and offers a better user experience by significantly reducing latency and providing swift access to relevant information.
It has five key use cases.
Question Answering
RAG allows for much better question answering capabilities within a specific context.
For example, a GenAI app that was built to help new employees learn about an organization’s codes of conduct would be able to provide answers to questions based on the company’s own documentation.
RAG complements the LLM’s overall understanding of codes of conduct with specific details relevant to a particular setting.
Reducing hallucinations
As mentioned, RAG improves accuracy when querying an LLM about something specific to a task or an organization.
But RAG can also improve factual accuracy when it comes to queries about information already contained in the dataset the LLM was trained on.
This is because the RAG model narrows down what would otherwise have been an unbounded question answering task.
Personalization
RAG augments prompts with the user’s personalized data.
Not only does this mean an improved user experience, making the user feel more like the system is addressing them specifically, it can also mean the system is able to adjust its answers based on what it knows about the user from their personal information.
Contextual answers
RAG allows a GenAI app to work effectively as a co-pilot by including data drawn from real-time user activity.
An app built to help users learn to code, for example, can include information about the code the user just wrote when making suggestions for the next step.
Working in highly specific industries
Training data for LLMs often doesn’t include heaps of information on highly specific areas like healthcare, financial services, or legal discovery.
As such, most commercial or open source LLMs will struggle to provide accurate responses to questions about legal precedents or treatment options.
RAG boosts the LLM’s knowledge base with access to resources specific to these areas. Equipped with these additional resources, AI can revolutionize industries.
It can even give your system extra capabilities, like multimodality to allow it to generate responses in a variety of media, or additional filters that increase the LLM’s accuracy and speed even further.
Using RAG in your GenAI project means you can supercharge what you offer your users.
Instead of a system that can provide general responses based on patterns of human behavior, your app will be able to give specific responses based on your data and user activity.
