Example 1.
A law firm could use a gar in an AI system to:
search for relevant jurisprudence, precedents and judicial decisions in documentary databases during the investigation.
Generate case summaries by extracting key facts from prior records and resolutions.
Automatically provide employees with relevant regulatory updates.
Example 2: real estate agency
A real estate agency could use a gar in an AI system to:
summarize data from real estate transaction oman mobile phone number histories and neighborhood crime statistics.
Answer legal questions about real estate transactions by citing local real estate laws and regulations.
Streamline appraisal processes by extracting data from property condition reports, market trends, and historical sales.
Example 3: eCommerce store
An e-commerce company could use a gar in an AI system to:
Gather information about products, specifications, and reviews from the company's database to develop personalized product recommendations.
Retrieve order history to generate personalized shopping experiences tailored to user preferences.
Generate targeted email campaigns by retrieving customer segmentation data and combining it with recent purchasing patterns.
Advantages of gar
illustration of man in suit with bow and arrow aiming at target.
As anyone who has consulted chatgpt or claude knows, llms has minimal safeguards built in.
Without proper monitoring, they can produce inaccurate or even harmful information, making them unreliable for real-world deployments.
The gar offers a solution by basing responses on reliable and up-to-date data sources, significantly reducing these risks.
Avoid hallucinations and inaccuracies
Traditional linguistic models often generate hallucinations, that is, answers that sound convincing but are objectively incorrect or irrelevant.
Gar mitigates hallucinations by basing responses on reliable and hyper-relevant data sources.
The retrieval step ensures that the model references accurate and up-to-date information, significantly reducing the possibility of hallucinations and increasing reliability.
Retrieve updated information
Although llms is a powerful tool for many tasks, it is unable to provide accurate information on rare or recent data, including tailored business insights.
But rag allows the model to get real-time information from any source, including websites, tables, or databases.
This ensures that as long as a source of truth is updated, the model will respond with updated information.
Communicate in complex contexts
Another weakness of traditional LLM use is the loss of contextual information. Llms has difficulty maintaining context in long or complex conversations. This often results in incomplete or fragmented responses.
But a rag model allows you to understand the context by extracting information directly from semantically linked data sources.
With additional information specifically targeted to users' needs - like a sales chatbot equipped with a product catalog - rag allows AI agents to engage in contextual conversations.
