Harness the Potential of Enterprise Knowledge Management with RAG Technology and the ChatGPT Model
Corporate knowledge management is a key element to the success of any modern business. Well-structured, accessible and reliable knowledge can make the difference between a company that thrives and one that struggles to stay competitive.
In this article, we will explore an innovative solution based on RAG technology (Retrieval Augmented Generation) and the ChatGPT model, which promises to revolutionise the management of relationships between corporate knowledge and internal and external users.
The Basics: What is the ChatGPT Model?
The heart of this solution is the ChatGPT model, a member of the GPT (Generative Pre-trained Transformer) family. These artificial intelligence models have proven to be extraordinarily capable in understanding a request and generating coherent text to answer questions based on the provided context. The ChatGPT model has been specifically trained with thousands of GB of knowledge acquired from everything that was digitally accessible.
But what really makes this solution unique? Let’s look at some of its distinctive features.
The Power of Vector Databases
At the heart of this solution is the use of a vector database. This database represents words and phrases as points in a multidimensional space. Why is this so important? It allows the ChatGPT model to calculate the similarity between word vectors and select the most appropriate words to answer a question. Imagine needing to find specific information in a vast ocean of textual data; the vector database acts as a compass, guiding you to the desired answers.
Similarity Search: Finding the Needle in the Haystack
One of the most important tasks of this solution is similarity search. This functionality allows the ChatGPT model to find similar words or phrases based on the input provided by the user. Imagine searching for a needle in a virtual haystack of business information. Similarity search is like a magnet that attracts the needle, helping you to easily identify relevant information.
The Construction of Context through Prompts
Another crucial element is the construction of context through prompts. The user provides a prompt or a question, and the ChatGPT model uses this input to generate a response. The context provided by the user plays a fundamental role in determining the response generated by the model. This means that the formulation of clear and specific prompts is essential for obtaining accurate results.
High-Quality Data: The Cornerstone of Knowledge
The quality and quantity of available data are fundamental to the success of the ChatGPT model. Imagine building a house: high-quality data are the cornerstone on which everything is based. A vector database, populated with structured information, is the key to achieving accurate and reliable results.
Differences between relying on generic knowledge and your own business data: the RAG model
Retrieval Augmented Generation (RAG) represents a key solution for addressing two crucial aspects in business information management and response quality.
the protection of sensitive data
the provision of accurate and exact answers without the risk of “hallucinations”.
Business data protection: A fundamental aspect of enterprise knowledge management is the protection of the company’s sensitive or confidential data. With the use of AI models like OpenAI’s Chat GPT, there is concern that business information could be exposed or shared with third parties. The RAG solves this problem as it maintains full control over business data. In particular, the vector database, which contains the company’s structured information, is hosted internally (“on premise”), ensuring that business data remains secure and is not shared with external vendors or third-party platforms.
Providing accurate answers without hallucinations: Another crucial aspect is ensuring that the answers provided to employees or business clients are accurate and free from errors or “hallucinations”. The RAG addresses this challenge through a rigorous process. When a user asks a question, the system uses an intent definition engine and compares the question with the vector database. This comparison is based on the logic of similarity search, meaning that the answers are selected based on how similar they are to the information present in the company’s structured database. This ensures that the answers are accurate and relevant to the business information, minimising the risk of incorrect or misleading answers.
Inoltre, il RAG consente di sfruttare appieno la conoscenza aziendale strutturata di EKR, la stessa precedentemente utilizzata per creare documenti tradizionali come cataloghi, listini, data sheet, manuali e troubleshooting. Questi dati strutturati sono fondamentali per il processo di risposta generata dall’IA, poiché forniscono la base per la costruzione di risposte accurate e contestualmente rilevanti. Utilizzando queste informazioni già vettorializzate e organizzate dalla Knowledge base strutturata dell’azienda, il sistema può generare risposte precise e coerenti che riflettono fedelmente le informazioni aziendali.
By leveraging vector databases, similarity search, well-constructed prompts, and high-quality data, companies can obtain accurate and reliable answers to their questions posed to AI, as if they were asked to a DIGITAL EXPERT.
But remember, knowledge is the key, and the road to business success passes through intelligent knowledge management. EKR has been helping companies manage knowledge in a structured way with the EKR Orchestra method for 15 years.
