LEARNING OBJECTIVE
This guide will provide you with a good overview of what RAG is and how your company can use it to generate accurate insights from your enterprise content.
PRE-REQUISITES
You should already be familiar with basic generative AI concepts.
LET'S BEGIN!
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RAG OVERVIEW
Retrieval-Augmented Generation (RAG) is a technique that enhances responses from AI by pulling in relevant information from trusted sources, making answers more precise and tailored to specific needs. This means that RAG can access relevant, up-to-date information from various sources, such as company databases or online knowledge bases, and use it to generate detailed and insightful responses to specific questions.
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TERMS YOU SHOULD BE FAMILIAR WITH
Generative AI
Artificial Intelligence models that can create new content, such as text, images, or audio, based on patterns they’ve learned from existing data.
Large Language Model (LLM)
A type of AI that understands and generates human language.
Knowledge Base
A structured repository of information that the RAG system can access to retrieve relevant data for generating responses. Sources can include Google Drive, Microsoft SharePoint, emails, calendar events and PDFs.
Retrieval System
The component of RAG responsible for finding and retrieving relevant documents or pieces of information from a knowledge base to enhance the user’s prompt, which is then fed into the generative AI engine for a more contextually relevant response.
Contextual Relevance
Refers to how well the retrieved information aligns with the user’s question, ensuring that the AI's response is meaningful and directly applicable.
Fine-Tuning
Fine-tuning in RAG helps customize both the information retrieval and the way responses are crafted, ensuring that the system not only finds the right information but also delivers it in a way that fits the business’s unique requirements.
Prompt Engineering
The practice of designing effective inputs (prompts) for the AI to generate the desired response. Good prompt engineering helps to refine the accuracy and relevance of outputs in RAG systems.
Document Embeddings
Numerical representations of documents that capture their semantic meaning, enabling the retrieval system to find relevant information more efficiently by comparing embeddings. This is done at the ingestion phase of RAG.
Inference
The process through which the AI model uses the retrieved data to generate a response that is relevant and context-specific. The model takes the information provided by the retrieval system and synthesizes it with its own knowledge to produce an answer.
Latency
The time it takes for the RAG system to retrieve information and generate a response.
Natural Language Processing (NLP)
The technology that allows computers to understand, interpret, and generate human language. NLP is a key component in enabling RAG systems to understand queries and provide relevant responses.
Data Source Integration
The process of connecting various internal and external data sources to the RAG system.
Query Understanding
Refers to the RAG system’s ability to correctly interpret a user’s question, which is critical for retrieving the most relevant information and generating accurate answers.
Tokenization
The process of breaking down text into smaller units, called tokens, which can be words, phrases, or even individual characters. This helps the AI understand and process the text by transforming it into a format that it can work with. It allows the model to manage and understand text in a structured way, making it possible to accurately match user queries with relevant information and generate relevant responses.
Business Insights
Actionable knowledge that the RAG system can generate from data.
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BENEFITS FOR BUSINESSES
Improved Accuracy
Retrieves real-time information, reducing outdated or incorrect responses.
Enhanced Efficiency
Automates data retrieval and synthesis, saving time on manual searches.
Better Decision-Making
Access to company data sources enables more informed and strategic decisions.
Scalability
Handles large volumes of data from multiple sources, supporting growth and adaptability.
Customization and Flexibility
Can be tailored to specific business needs.
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BENEFITS FOR CUSTOMERS
Personalized Interactions
RAG can access specific customer data and preferences, enabling more personalized and relevant responses.
Faster Response Times
By quickly retrieving relevant information, RAG can provide immediate, accurate answers, reducing wait times and improving the overall customer experience.
Consistent Service Quality
With access to up-to-date information, RAG ensures that customers receive consistent and accurate responses, regardless of the channel they interact in.
Proactive Support
RAG can analyze past interactions and customer data to anticipate needs and offer proactive solutions.
Enhanced Self-Service Options
By integrating RAG into self-service platforms like chatbots or knowledge bases, businesses can empower customers to find solutions on their own.
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CHALLENGES
Data Quality Dependency
Relies on high-quality and relevant data; poor data can lead to inaccurate results (GIGO).
Integration Complexity
Involves ensuring compatibility across various data formats, systems, and sources, which can be challenging to implement. This often means connecting multiple systems—such as different LLMs, internal sources and vector databases—to work seamlessly together.
Latency Issues
Retrieval processes can slow response times, especially with large datasets.
Data Privacy Concerns
Access to multiple data sources can raise issues around data privacy and regulatory compliance.
Maintenance Requirements
Ongoing updates and maintenance are needed to ensure efficiency and accuracy, requiring dedicated resources.
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COST CONSIDERATIONS
Cost is a significant factor when implementing RAG systems, as there are expenses associated with document ingestion, token processing, and infrastructure. Ingesting large volumes of data into vector databases can often incurs storage and compute fees, especially when frequent updates are necessary. Additionally, LLMs charge based on token usage, which can quickly add up with extensive retrieval and generation operations. Beyond this, maintaining the infrastructure required to support multiple databases and ensure seamless data retrieval can lead to additional storage and operational costs. Businesses should carefully evaluate these costs before implementing a RAG system in their company.
RECAP
In this guide, you learned what Retrieval-Augmented Generation is, the benefits to both businesses and customers, current challenges with it and costs to consider before implementing it.
NEXT STEPS
If you are interested in learning more about RAG, make sure to check out our upcoming live streams or our on-demand tutorials for more RAG related topics.
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