How Retrieval-Augmented Generation (RAG) Helps Reduce AI Hallucinations

AI technologies have proven to be one of the most impactful business tools of the past few decades. But they’re not perfect – especially regarding the ongoing issue of AI hallucinations. 

Retrieval-augmented generation (RAG) is an emerging AI technique that helps combat the nagging issue of hallucinations. RAG enhances the capabilities of existing AI models to perform specific tasks by providing them with new information outside their training datasets. 

But how does RAG work, what are its advantages, and how does it help eliminate AI hallucinations? Keep reading to find out.

What is Retrieval-Augmented Generation, and How Does it Work?

Unlike other AI refinement techniques such as fine-tuning or prompt engineering, RAG grounds large language models (LLMs) with the most accurate and up-to-date information possible in real-time. It involves asking the model to consider this additional information in its response by providing relevant, recent data and appending it as a few-shot prompt to improve model responses. 

Fine-tuning involves retraining models with more relevant or up-to-date information – a time-consuming and expensive task. Prompt engineering involves writing more descriptive prompts to improve results.

The major difference is the ability of RAG to provide additional, context-specific information to a model in real-time. That’s crucial because LLMs and other natural language processing (NLP) techniques are typically information-locked based on the recency and volume of their most recent training data. 

The real-time nature of the additional information RAG provides is why the technique is often used for chatbots, virtual assistants, and other applications where the accuracy and timing of responses are essential. 

RAG implementations are based on three distinct components:

1. Input encoder: The foundation of all other RAG components, the input encoder processes user prompts and transforms them into vector representations to capture the prompt’s semantic meaning. Usually involves a transformer-based architecture such as BERT or RoBERTa. 
2. Neural retriever: A bridge between the input encoder and the model’s knowledge base. The neural retriever uses input vector representations to search the knowledge base for relevant passages for contextual information. Uses a dense passage retrieval (DPR) approach to embed the prompt and relevant passages in a vector database such as MongoDB, Chroma, Pinecone, Weaviate, or Milvus. 
3. Output generator: This component crafts the AI output, such as generated text. The output generator combines the original prompt with the retrieved passages to provide a more accurate response. 

RAG techniques were initially introduced by the Facebook AI team in 2020 and are especially effective for tasks that require deep context or multiple complex sources. RAG is the underlying technique in Bing Search functionality, for example, and ChatGPT prompt repositories such as AIPRM.

Why Do AI Hallucinations Occur?

AI models are only as effective as the data they’ve been trained on. LLM outputs are non-deterministic and based on probabilities, not actual knowledge or consciousness. When LLMs such as ChatGPT are queried on nuanced topics outside their knowledge base, they often make up answers and sources.

“If (LLMs) occasionally sound like they have no idea what they’re saying, it’s because they don’t,” explains the IBM Research blog. “LLMs know how words relate statistically, but not what they mean.”

These hallucinations can be harmless – even amusing – in some circumstances. One New York Times journalist even had Bing’s AI chatbot declare its love for him (while asking him to leave his wife so they could be together) in early 2023. 

But AI hallucinations can also be embarrassing or even devastating in other scenarios, such as content creation for a public audience or healthcare.  

AI hallucinations generally have three leading causes:

• Overfitting occurs when models are extremely complex or training data is especially noisy or insufficient in scope. The models learn the outliers and noise within the data, leading to low-quality pattern recognition, which in turn can lead to classification, prediction, and factual errors along with hallucinations.
• Data quality issues include mislabelling, miscategorization, or bad classification of data, which can lead to AI bias, errors, and hallucinations. For example, if an image of a sedan is accidentally classified as a pickup truck, the model could later return an answer indicating that sedans are excellent for tradespeople who need to haul or tow large loads to a job site.

• Data sparsity, which is a lack of fresh and relevant data to train the model, can also cause hallucinations by encouraging the model to fill knowledge gaps on its own (which almost certainly will result in wrong answers).

Hallucinations and other errors, which in most cases sound completely plausible, directly erode user trust in LLMs, leading to weaker user uptake and diminished impact. They can also drive misinformation or lead to unwanted or even dangerous situations, such as unnecessary medical procedures, if the information is taken at face value.

What Are the Advantages of Retrieval-Augmented Generation?

AI experts have described the difference between RAG and other AI approaches as “the difference between an open-book and closed-book exam,” because it has access to additional information as it works.

Because of this, RAG has several inherent benefits to users, including: 

• Helping tame AI hallucinations and improving the factuality and relevance of AI-generated responses.
• Providing models with the most recent proprietary and contextual data – an approach that has been proven to reduce AI hallucinations and lower the risk of a model leaking sensitive data.
• Providing greater transparency to users around how the model generated a particular output, by ensuring users can access the model’s sources (this also allows for more efficient fact-checking).
• Reducing the need for model parameter updates and ongoing model training on new data, helping lower the cost of running LLMs in a business setting (for applications such as chatbots).

As mentioned above, RAG must be combined with an operational data store that turns queries into numerical vectors (a vector database). The vector database allows the model to dynamically learn and adapt on the fly, improving the model’s understanding in real-time.

CapeStart: Your AI and Machine Learning Experts

CapeStart’s legions of data scientists, machine learning engineers, and other AI and data annotation experts can help you leverage RAG with NLP and LLMs to develop world-class chatbots, virtual assistants, and other AI-powered applications. 

Contact us today to schedule a one-on-one discovery call with one of our experts.