GenAI generates images, text, videos, and other media in response to inputted prompts, but ensuring that these outputs are accurate is a mighty challenge. Since Large Language Models (LLMs) generate text based on patterns learned from vast datasets, and don’t understand truth or reality they can produce misleading, factually incorrect, or entirely fabricated responses. 

LLM Hallucinations

LLM Hallucinations refer to responses where the model confidently presents information that may seem plausible, but is entirely unsubstantiated or false. These hallucinations can occur in text, image generation or any other output, and can range from subtle inaccuracies to outright falsehoods, and are often undetectable without a thorough verification procedure. The AI hallucination rate is an important metric that quantifies how frequently these errors occur, and understanding this rate is crucial for improving AI systems. 

The issue of LLM hallucinations has practical implications for business, especially as these models become more integrated into various sectors, including GenAI, in enterprise applications. The reliability of LLM responses is crucial in maintaining trust in the organization’s credibility and user satisfaction. Trustworthy responses can prevent bad and potentially harmful decisions, such as in a medical diagnosis app. Needless to say, ethical and legal concerns are at the heart of ensuring that AI systems are used responsibly and fairly.

Limitations of LLMs

As the usage of LLMs quickly expanded in the past two years, different methods to analyze and measure LLM performance were created. Specific limitations of the straightforward ‘prompt to output’ implementations became evident:

• Knowledge is frozen at the time of training, and the model can’t verify the accuracy of their outputs
• Responses with false data (hallucinations): LLMs lack real-time access to updated information, and may provide wrong or outdated information, or in a format that is not useful for humans
• Biased responses: may provide tilted responses due to using biased information for training
• Context limitations: may lose track of long or multi-stage conversations.
• Lacks specialization: less accurate for specific tasks or detailed queries that are missing in the training set
• Security gaps: attackers may trigger responses that expose confidential data, or may confuse the model
• Resource-heavy: LLMs require substantial computation power, large memory, and substantial electric energy to train and to run the models

Different approaches to reducing hallucinations

Organizations and researchers have developed several strategies to address these limitations and improve the accuracy of generative AI outputs. Let’s explore the main approaches:

Custom Model Tuning

Custom tuning involves additional training of the base model on domain-specific data. The purpose of fine-tuning is to adapt the model to perform better in specific scenarios or on tasks that were not well covered during pre-training. While effective, this approach has significant drawbacks, including:

• High computational costs, and may take weeks or months to implement
• Requires large amounts of high-quality training data 
• May need regular retraining as information changes

Prompt Engineering and Enrichment

Prompt engineering focuses on crafting better instructions and context for the model and augmenting the input prompts, especially to deal with lack of accuracy, using:

• More detailed and specific prompts that include relevant context directly in the prompt
• Structured output formats
• System messages to guide behavior

While this approach is more accessible than custom tuning, it has limitations, such as prompt size constraints, increased token usage and costs, lack of scalability and complexity in maintaining prompt libraries. Models do not yet understand nuances or have the contextual understanding based on implicit knowledge; instead, they generate responses based on learned patterns during training. 

Supervised Fine-Tuning (SFT)

This method refines a model by training it with task-specific data through supervised learning. SFT is a useful tool for aligning language models and is simple and inexpensive, which has made it popular within the open-source LLM research community and beyond. 

RAG Technology: Enhancing GenAI Accuracy Through Knowledge Integration

A different approach to reducing LLM hallucinations and improving the accuracy of GenAI responses is that posed by Retrieval Augmented Generation (RAG). The concept of RAG: Augmenting the query by Retrieval of relevant documents and Generation of an accurate response. This concept addresses many of these limitations by supplementing foundational LLMs with a mechanism to extract data from dedicated domain-specific knowledge bases. RAG works by retrieving relevant documents and generating more accurate responses. RAG has emerged as a powerful solution that combines the best of both worlds – the language understanding capabilities of LLMs with direct access to current, accurate information. In a RAG system, a model first retrieves relevant documents or data from a large corpus based on a given query, and then uses this retrieved information to generate a more accurate and contextually rich response, as seen in this 10,000 foot overview: 

LLMs enhanced by Retrieval Augmented Generation

This hybrid approach leverages both the capabilities of retrieval-based and generative models, aiming to enhance the overall performance of AI systems. Unlike traditional AI models that generate responses based solely on their training data, RAG integrates active retrieval mechanisms to access and incorporate external, domain-specific information into the generation process.  

RAG unites two critical components in AI models: data retrieval and language processing. This integration enables the extraction and conversion of complex data into a format that aligns with human understanding. This combination of retrieval and generation also ensures that responses are contextually relevant, accurate, and up-to-date, making it a powerful tool for enterprise environments.

Key Components of RAG Architecture

RAG architecture consists of several crucial components working together, including:

• AI Model: initially, it retrieves relevant documents or pieces of information from a predefined database or knowledge base, then uses this information to generate coherent and contextually accurate responses
• Document Processing Pipeline: extracts text from various sources and chunks documents into manageable segment, then cleans and normalizes the content
• Vector Database: adept at handling multi-dimensional data, often referred to as vector embeddings. These embeddings translate complex, unstructured data into a format that machines can interpret and process. Embedding enables efficient storage and indexing and fast similarity search capabilities
• Retrieval System: uses query understanding and processing, semantic search implementation and relevance scoring and ranking
• Context Integration: dynamic prompt construction with context window management and source attribution tracking

Incorporating structured and unstructured data with RAG technology 

RAG is able to work with unstructured data, to process diverse content types such as internal documentation, emails, meeting transcripts, customer feedback forms and social media. This technology is able to convert complex data into natural language effectively, since it understands context and nuance and can process conversational content. Unstructured data can provide additional context and background information that can help the language model generate more nuanced and informative responses.

RAG pipelines can also incorporate structured data, retrieving relevant structured data and generating cohesive reports or explanations. Structured data provides factual information that can be directly incorporated into the LLM’s response, reducing the risk of hallucinations or inaccurate information. By using structured data sources like product catalogs or customer databases, RAG can generate more relevant and personalized responses to user inquiries.

By effectively combining RAG with structured and unstructured data, LLMs can improve the relevance of the data with a better understanding of the query, and provide more relevant information. Structured data provides factual information, while unstructured data provides context and nuance. With this technology, organizations can unlock the full potential of AI to drive innovation, improve decision-making, and enhance customer experiences.