Introduction
The TechCrunch article “Why RAG Won’t Solve Generative AI’s Hallucinations Problem” makes a splash with its provocative title. But does it tell the whole story? While the article raises valid concerns about Retrieval Augmented Generation (RAG), it’s important to look beyond the limitations and consider the bigger picture. Could RAG, when implemented thoughtfully, be a key player in the quest for more reliable AI?
What is RAG anyway?
Before we dig too deeply into why there are reasons to be encouraged by RAG as a solution to AI hallucinations, let’s recap what it is and how it works. RAG stands for Retrieval Augmented Generation. What does that mean? Below is a technical explanation and then a simple analogy. Feel free to skip either one based on your familiarity with AI.
Technical Explanation of the RAG pipeline
- Query Embedding: The user’s query is converted into a numerical representation called an embedding, which captures its semantic meaning.
- Document Retrieval: The query embedding is used to search through a vast knowledge base, retrieving the most relevant documents based on their similarity to the query.
- Document Embedding: The retrieved documents are also converted into embeddings, allowing the AI to process their content numerically.
- Context Fusion: The query and document embeddings are combined to create a fused context, representing the most pertinent information from the external knowledge.
- Generation: The fused context is fed into the language model, which uses it as additional input to generate a response that incorporates the retrieved knowledge.
- Response: The generated response, now informed by the external knowledge, is returned to the user, providing more accurate and contextually relevant information.
The simpler explanation
We really love the analogy of a student taking a test. Think of a Large Language Model as a student. Your average student learns a vast amount of information throughout their lives, from the earliest days in preschool, all the way through high school (this is their “pre-training”). During exam time, the student is tested on their general knowledge about history or math, etc., and needs to rely on their ability to recall information that they learned throughout their lives. When the student comes across a test question they don’t know, they can either leave the answer blank (refuse to answer the question), they can guess, or they can make up the answer (in the world of AI, we call it confabulate or hallucinate the answer).
Now let’s think of an open-book test where the student is allowed to reference textbooks and other resources to help augment their knowledge. This is essentially how RAG systems work. They are like students that have access to additional resources. Instead of hoping that ChatGPT was trained on exactly the information you need, it is given an open book (such as your business data in the form of PDFs, Documents, YouTube videos, and more) in order to answer your specific questions.
The Right Tool for the Right Job
Now let’s get back to the TechCrunch piece and why we think it doesn’t full consider the facts, especially when it comes to CustomGPT.ai. The article focuses heavily on RAG’s shortcomings in “reasoning-intensive” tasks like coding and math. Fair enough, but let’s be real: RAG isn’t designed for those heavy-duty tasks. It’s like criticizing a screwdriver for not being a hammer – it misses the point. RAG shines in “knowledge-intensive” scenarios, where the goal is to find specific, factual information. Think customer service bots answering questions about products or research assistants digging up relevant data.
The Hallucination Buster (Well, Mostly)
Let’s be clear: RAG isn’t a magic bullet for AI hallucinations. The TechCrunch article is right about that. However, RAG, when used strategically, can significantly reduce hallucinations by grounding AI responses in verified information. It’s a powerful tool in the arsenal against AI making stuff up. For example, CustomGPT.ai has developed an innovative solution called the “Context Boundary” feature, which constrains AI responses to verified business data, further mitigating the risk of hallucinations. In this instance, you have a RAG system that is designed to simply admit if it doesn’t have access to the information related to a question and refuses to answer instead of hallucinating a made up response. These kinds of guardrails, while fundamentally different from a typical AI-powered chatbot, are a real-world solution to hallucinations. In addition, this system will also provide citations along with the output so there is no guessing where the response came from. Citations add a critical layer of confidence since the user can simply review the citation to find where the information was pulled from that comprises the answer.
A Reality Check for Vendors (and Us)
Still, the TechCrunch article serves as a much-needed reality check for vendors who might overhype RAG’s capabilities. It’s a reminder that honesty and transparency are key. By acknowledging both the strengths and limitations of RAG, vendors can empower businesses to make informed decisions about how to best leverage this technology. It’s also crucial to set clear boundaries for AI responses and ensure that they are derived solely from reliable business content. AI powered chatbots are really amazing, in part because they can hallucinate! But for businesses with mission critical use cases such as in medicine and law, hallucinations represent a very real risk. That’s why locking down the tendency for outputting creative responses and forcing the model to only rely on source material is a very elegant and useful solution to hallucinations.
Conclusion: The Future of RAG is Bright
While the TechCrunch article highlights the challenges RAG faces, it’s important to remember that this technology is still evolving. As research progresses, we can expect even more sophisticated versions of RAG that push the boundaries of what’s possible. In the meantime, let’s celebrate RAG for what it is: a valuable tool that, when used wisely, can make AI more reliable, trustworthy, and ultimately, more useful.
