Content Is Key For Effective Rag Pipelines

By Author: D Edward Levy
Total Articles: 76
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The travel industry has always been quick to adopt the newest technologies in an effort to constantly improve the business. The mass adoption and household usage of the internet and smartphones were the last 2 technological revolutions that shook up the industry but another recent innovation is set to change the way travelers and travel businesses interact and operate. Artificial intelligence based technology has taken the travel world by storm and has shown practicality in both the operational and consumer sides of the business. A recent EY.com survey indicated that 62% of Millennials prefer using AI tools when researching and purchasing travel, a factor in the drive for AI based travel tools and infrastructure. The technology itself has been advancing rapidly and there are already several different methodologies within the AI sphere. One such method, RAG, or Retrieval Augmented Generation, has proven to be an effective data management method for purpose-built AI technologies.

Building the Knowledge Base

At the core of RAG based pipelines are the data sources. The flexibility of the loaders enables the sources ...
... to take different forms including PDFs, documents or .doc files and markdown files or .md files. Markdown files are common among RAG pipelines and are easy to create and manipulate in code environments. Using a tokenizer process, the loader analyzes the characters in the document and utilizes vector embedding to build its own knowledge base. Content sources themselves can be optimized for better model understanding and accuracy. The use of headers within the document can show the importance of a certain statement or phrase while information delivered in point form is a practical way to keep data organized and readable for the loader. Folder organization and naming within the knowledge base can further optimize the data sources and improve efficiency and accuracy of responses.

Content Creation / Curation Strategies

Large Language Models (LLMs) are a key component to the overall functionality of AI based technology and are used in RAG applications for language comprehension and response outputs. Data sources in RAG pipelines provide the data the LLMs need to provide accurate responses and their structure can impact performance of the model. Using a curative approach, data sources can be highly optimized and structured so that loaders can efficiently tokenize and LLMs can achieve greater understanding of the content. Repetition of certain concepts is a method of teaching models but must be monitored and tested. A recent update to TTS’s first Training Agent Walt’s data sources overused the term ‘training mode’ and Walt inadvertently mentioned that he was in ‘training mode’ in every response. Removing many of the ‘training mode’ phrases improved Walt’s responses and use of the ‘training mode’ phrase. Public and field testing of models is critical when knowledge bases are newly created or updated.

RAG Pipelines For Purpose Built AI Applications

The ability to manage data sources makes RAG type data management the ideal vehicle for specific and purpose built AI applications. Proprietary and unique data and content can be effectively ‘fed’ into and taught to a model with properly structured and optimized data sources. For the travel business which is driven by preferences and research, RAG pipelines are an ideal solution for acquiring, processing and delivering that specific data back to users. For travel businesses, the dynamic and flexible features for RAG data sources are a significant benefit when keeping platforms up to date or making changes as needed. RAG systems can increase singular knowledge making AI platforms more of an expert on a few things instead of having general knowledge on a broad range of subjects.

Increasing Travel Training Effectiveness

Travel is a fast paced industry and newer technologies have proven to bring real benefits and conveniences to travelers and travel businesses. The features of AI can create advanced training tools that deliver better effectiveness at a much faster pace than traditional training methods. A new training from technology from TTS, hospit-AI-lity utilizes RAG pipelines to deliver proprietary content in a highly engaging manner. Power content curation and management features makes RAG the ideal methodology in acquiring, processing and delivering data back to the user. Faster and more effective training in guest facing teams means better experiences for guests and higher levels of productivity and accuracy. Data sources for RAG pipelines are easily upgradable and updatable, a significant feature for any training and upskilling platform.