In the space of enterprise knowledge management, there are key challenges to overcome while leveraging large language models (LLMs). While LLMs are trained on vast amounts of data and thus, incredibly powerful, these models often struggle with providing accurate answers based on specific domains or private organizational data. Unlike everyday use cases, the cost associated with incorrect responses from LLMs is far more significant in a business context. Because it is impractical to train these models on every organization’s internal knowledge base, we turn to Retrieval-Augmented Generation (RAG) to extend the already powerful capabilities of LLMs to specific domains without the need to retrain the model.
Thus, we say Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model (LLM) by referencing an authoritative knowledge base outside its training data before generating a response.
Hallucinations in Answers
When facts are not available to them, LLMs extrapolate to give often false but plausible-sounding answers. Such inaccurate responses from LLMs are called “hallucination”, and can be particularly problematic for enterprises. Unlike casual use, where a wrong answer might be trivial, in a business environment, it can lead to costly mistakes and misinformation. This makes the need for accurate and reliable information retrieval paramount.
The Complexity of Fine-Tuning
Fine-tuning LLMs with specific internal data is an option, but it poses major challenges. It is expensive, requires intricate knowledge and significant resources, and involves training on sensitive data, which raises security concerns. These security concerns often discourage organizations from adopting new AI tools.
Resource-Intensive Setup and Integration
Customizing an LLM to cover multiple enterprise goals is both expensive in terms of workforce and maintenance. It involves the most expensive employees from IT and engineering and is not an out-of-box-solution. These tasks often distract focus away from core product value, adding another layer of complexity to the already resource-intensive process.
The Mechanism of RAG
Retrieval Augmented Generation (RAG) addresses the challenges of using LLMs in enterprise settings by adding an extra layer before generating a response. This layer involves the LLM searching a designated database first, using user-defined metrics like recency, permissions, and customized rules to identify the most accurate information. This ensures responses are precise and relevant to the enterprise context.
Avoiding Misleading Information
RAG allows enterprises to define and rank data sources for LLMs to consume, which can significantly help avoid incorrect or misleading information. This means employees receive accurate and reliable answers without the need for frequent and costly retraining. RAG's quick indexing of new information ensures that updates are incorporated quickly and effectively.
Scalability and Efficiency
Unlike fine-tuning, which is impractical and resource-intensive due to the need for constant updates, RAG offers a scalable and efficient solution. Information retrieval is updated within minutes, eliminating the need for continuous LLM retraining and providing a sustainable option for enterprises.
Focus on Core Business
Unleash’s AI tool offers a production-grade solution that allows your team to keep the focus on primary business activities rather than deploying extensive resources on AI research, development, and training. With Unleash, you can very well unleash the full power of AI for your business growth without the associated complexities.
Out-of-the-Box Integrations
Unleash RAG AI Model comes with over 70 integrations for popular software platforms. This is key as it enables seamless indexing of information from tools you already leverage day-to-day like Google Drive, Confluence, Sharepoint, Notion, Jira, and others. Unleash allows you to interact with its AI tool from different environments where you spend most of your time, such as Slack, Zendesk, Salesforce or Unleash’s native app.
No Data Training Required
As opposed to directly using LLMs to train on your data, Unleash’s AI tool does not train on your organization’s sensitive data.
Instead, it utilizes Retrieval Augmented Generation to harness the power of AI models like GPT (ChatGPT) to locate information within your specific database. This process ensures there is no danger of information leak, your data remains secure, and that the AI model does not require training on sensitive information.
Conclusion
Implementing Retrieval Augmented Generation (RAG) addresses the significant challenges posed by large language models (LLMs) in the realm of enterprise knowledge management. By incorporating RAG, businesses can overcome issues like hallucinations, the complexity of fine-tuning, and resource-intensive setups. RAG ensures accurate, relevant, and useful information retrieval without compromising data security or requiring extensive retraining.
Unleash offers an AI-based RAG solution that integrates seamlessly with over 70 popular software platforms, enabling enterprises to harness the power of AI models without the aforementioned complexities. Unleash allows you to focus on core business activities while benefiting from more precise and efficient AI-driven information retrieval. This makes Unleash RAG AI an invaluable tool for enhancing your teams’ productivity and maintaining a competitive edge in today’s tech-driven business environment.
