Artificial intelligence has made massive strides, and most of us have reaped the benefits. Initially emerging through theoretical frameworks with limited practical applications, AI systems evolved into sophisticated generative solutions, thanks to computational power advancements, machine learning algorithms, and increased web data availability.

We have already witnessed generative AI’s integration into various applications, from language translation and content generation to virtual chat assistants and creative tools. However, the technology still faces limitations. The effectiveness of techniques utilized for training and refining underlying models hinge on the quality and diversity of training data, which is difficult to collect due to data privacy and copyright concerns. Moreover, human-produced data is rife with historical biases and inaccuracies, resulting in poor AI outputs and hallucinations.

Static training datasets limit AI’s potential

Static training datasets often fail to capture the breadth and diversity of real-world representations, diminishing the AI outputs’ relevance and accuracy. Hallucinations and inaccuracies haunting current generative solutions frequently occur due to the absence of real-time data integration. Apart from spreading misinformation, these limitations negatively impact user trust and overall AI utility.

Moreover, current models struggle with handling complex queries that require nuanced contextual understanding or domain-specific knowledge. This effect is further compounded by their lack of adaptability to evolving trends, topics, and user preferences.

To cut the costs of training large language models (LLMs), developers often resort to synthetic data generated by AI systems themselves. However, this practice might result in what some researchers call an AI echo chamber, an ouroboros effect that further degrades the model’s capabilities. How does this happen?

Models based on synthetic data continuously receive information depicting a repetitive and likely reality, leading to overestimating the probability of these outcomes while downplaying improbable ones. Over time, this process causes the model to disregard outliers entirely, diminishing its comprehension of potential scenarios.

Dynamic information retrieval

Retrieval-augmented generation (RAG) is a method of optimizing LLM outputs, combining underlying generative models with information retrieval techniques that gather data from external knowledge sources. Compared to another common LLM output optimization approach, fine-tuning, RAG’s dynamic information retrieval reduces reliance on static datasets while offering greater agility and flexibility in AI applications.

By incorporating up-to-date and diverse information from the open web or other external repositories, RAG enhances the relevance and accuracy of AI-generated outputs while, in some cases, also mitigating the LLM hallucination issue. Moreover, using RAG, developers can complement internal LLM knowledge with specific data from chosen domains (i.e., open industrial data, academic research data, news data, etc.).

Leveraging real-time public data from the web additionally ensures that AI systems can adapt to evolving trends, events, and contexts more easily, leading to improved user experiences, decision making capabilities, and reliability.

RAG-powered LLM applications can deliver personalized recommendations, more relevant answers to queries, and content that aligns with individual preferences and needs. This leads to enhanced user experiences as consumers receive tailored and insightful responses that address their specific concerns or interests.

Automated web data collection makes RAG possible

RAG applications retrieve information from a knowledge base, such as a vector database. To expand those knowledge sources and keep them relevant, developers often rely on web scraping technology that automates the extraction of large-scale online data. In most cases, this is publicly available information, but exceptions can exist in situations where developers are granted access to domain-specific information, such as industrial data.

Web scraping solutions can gather information from a wide array of diverse, contextually relevant sources, such as news articles, public forums, statistical repositories, etc. Furthermore, web scraping tools can be customized to target specific websites or databases, allowing RAG applications to extract the most relevant data points.

It is worth noting that modern web scraping solutions often involve data preprocessing steps, such as parsing, cleaning, and filtering, which help improve the quality and usability of the extracted information.

Currently, RAG offers probably the most effective way to enrich LLMs with novel and domain-specific data. This challenge is particularly important for such systems as chatbots, since the information they generate must be up to date. However, RAG cannot reason iteratively, which means it is still dependent on the underlying dataset (knowledge base, in RAG’s case). Even though this dataset is dynamically updated, if the information there isn’t coherent or is poorly categorized and labeled, the RAG model won’t be able to understand that the retrieval data is irrelevant, incomplete, or erroneous.

It would also be naive to expect RAG to solve the AI hallucination problem. Generative AI algorithms are statistical black boxes, meaning that developers do not always know why the model hallucinates and whether it is caused by insufficient or conflicting data. Moreover, dynamic data retrieval from external sources does not guarantee there are no inherent biases or disinformation in this data. For RAG to work, redundant or fabricated content must be filtered out.

All that glitters is not gold: RAG limitations

Therefore, RAG is in no way a definitive solution. In the case of sensitive industries, such as healthcare, law enforcement, or finance, fine-tuning LLMs with thoroughly cleaned, domain-specific datasets might be a more reliable option.

Final words

Generative AI’s limitations stem from its training techniques and available data quality. It often results in a lack of contextual understanding, generating incoherent responses or hallucinations. In response to these challenges, RAG dynamically integrates external knowledge sources into generative AI models, thereby enhancing their ability to produce more accurate and contextually relevant outputs.

However, all these benefits hinge on open data—a crucial component toward advancing AI technologies. To ensure that RAG models work effectively, developers must employ reliable data retrieval technologies and thoroughly evaluate that the data they load to the knowledge base is the necessary one, properly cleaned, structured, and labeled.

Julius Cerniauskas is CEO at Oxylabs.