How to build an efficient and effective Generative AI application
Generative AI empowers enterprises, but controlling outputs poses challenges. RAG framework grounds language models on verified facts, enhancing accuracy and reliability.
AI-assisted summary, reviewed by Hypthon
- Enterprise GenAI applications need relevant business context to produce useful responses.
- Retrieval Augmented Generation (RAG) adds retrieved knowledge to prompts, helping ground answers in enterprise information.
- Grounding can improve relevance and reliability, but it does not guarantee factual accuracy or prevent unsupported outputs.
GenAI & RAG
3 min read
By Hypthon
Benefits and flaws of GenAI
Generative AI, or GenAI in short, is a powerful machine learning algorithm that can generate realistic and complex images, text, audio and synthetic data. It works by learning patterns from existing data, and then using that information to mimic human activity and produce new content. The advancement in GenAI has revolutionized how enterprises operate and opened up new possibilities for solving business issues.
Organizations use AI to improve data-driven decisions, enhance omnichannel experiences, and drive next-generation product development.
However, GenAI alone is not enough to deliver the best outcome, as it may not achieve what the most impactful and effective solutions can do: showing the right message to the right user. A major pain point of GenAI is the lack of control and predictability over generated outputs.
Since these complex AI models can produce unwanted, inappropriate, or factually incorrect content that was not explicitly specified during training, it is difficult to ensure these AI applications are well-developed and can be used responsibly for beneficial purposes.
Makes it better with RAG
So, what can we do to quickly build a cost-efficient, scalable and trustworthy Generative AI application with data from the enterprise knowledge base?
To ensure Generative AI application responses are restricted to the enterprise knowledge base only, a technique known as Retrieval Augmented Generation (RAG) was introduced. RAG is an AI framework that grounds the large language model on a set of external verified facts to support its internal description of information, which greatly improves GenAI’s accuracy and reliability.
Practically, we can use RAG to retrieve data outside a foundation AI model, and augment the prompts by adding the relevant retrieved enterprise data in context. By integrating RAG to provide a focused enterprise context, the Generative AI application is able to generate more authentic and trustworthy responses, while restricting outputs to the relevant enterprise knowledge base only.
Figure 1: A high-level solution flow depicting how the AI application works
Figure 2: How the GenAI chatbot responds after restricting it to relevant enterprise knowledge base only
Enhance GenAI overall performance
In conclusion, unlike building your own AI model and fine-tuning an existing model, RAG facilitates the development of Generative AI applications at a lower cost, in an easier implementation, and with lower risks. It increases the relevance of responses and provides a better experience for users, revolutionizing traditional business operations.
Want to apply it to your business?
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