NEW! Download Gartner® Report: How to Improve and Optimize Retrieval Augmented Generation Systems. Get Started.

NEW! Download Gartner® Report: How to Improve and Optimize Retrieval Augmented Generation Systems. Get Started.

NEW! Download Gartner® Report: How to Improve and Optimize Retrieval Augmented Generation Systems. Get Started.

Retrieval Augmented Generation — RAG 101

RAG 101

Retrieval Augmented Generation — RAG 101

The Complete Guide

The Complete Guide

The Complete Guide

to

to

to

RAG + LLM

RAG + LLM

RAG + LLM

Tap into new value from your existing data by adding retrieval augmented generation.

Tap into new value from your existing data by adding retrieval augmented generation.

Tap into new value from your existing data by adding retrieval augmented generation.

Eliminate hallucinations to deliver complete, context-aware and trusted responses from your data.

Eliminate hallucinations to deliver complete, context-aware and trusted responses from your data.

Seamlessly integrate RAG chatbots with current applications to enable faster access to your information.

Seamlessly integrate RAG chatbots with current applications to enable faster access to your information.

Deploy RAG on your own data center, or SearchBlox’s secure fully managed cloud infrastructure.

Deploy RAG on your own data center, or SearchBlox’s secure fully managed cloud infrastructure.

Introduction

Introduction

Introduction

Make all user interactions with your data relevant and context-aware. Go beyond basic retrieval to providing contextual, personalized and up-to-date information from organizational data sources – wherever it lives.

Make all user interactions with your data relevant and context-aware. Go beyond basic retrieval to providing contextual, personalized and up-to-date information from organizational data sources – wherever it lives.

Make all user interactions with your data relevant and context-aware. Go beyond basic retrieval to providing contextual, personalized and up-to-date information from organizational data sources – wherever it lives.

As the adoption of Generative AI models like GPT-4, Llama 3 and Gemini increases, so does the increased scrutiny over the credibility of their responses. Large language models (LLMs), while impressive in their ability to generate highly coherent answers, are only as smart as the data used to train them. When questions fall outside their training sets, LLMs tend to regurgitate random facts or inaccurate information, otherwise known as “hallucination.”

As the adoption of Generative AI models like GPT-4, Llama 3 and Gemini increases, so does the increased scrutiny over the credibility of their responses. Large language models (LLMs), while impressive in their ability to generate highly coherent answers, are only as smart as the data used to train them. When questions fall outside their training sets, LLMs tend to regurgitate random facts or inaccurate information, otherwise known as “hallucination.”

We can use Retrieval Augmented Generation (RAG) to address the limitations of large language models – incorporating real-time, reliable information grounded in the latest internal data. RAG integrates your organization’s vast knowledge base—documents, databases, or any other relevant data source—with the LLM, enabling your AI applications to scour through specific data sets outside of its training domain.

We can use Retrieval Augmented Generation (RAG) to address the limitations of large language models – incorporating real-time, reliable information grounded in the latest internal data.

RAG integrates your organization’s vast knowledge base—documents, databases, or any other relevant data source—with the LLM, enabling your AI applications to scour through specific data sets outside of its training domain.

RAG is transforming the way businesses leverage their most valuable asset – knowledge. According to Gartner, the volume of synthetically generated marketing messages is set to explode in the coming years. RAG can play a pivotal role in ensuring these communications are engaging, factually correct, and relevant to each customer, enhancing trust and driving business outcomes.

RAG is transforming the way businesses leverage their most valuable asset – knowledge. According to Gartner, the volume of synthetically generated marketing messages is set to explode in the coming years. RAG can play a pivotal role in ensuring these communications are engaging, factually correct, and relevant to each customer, enhancing trust and driving business outcomes.

Imagine a customer service chatbot that instantly pulls up the latest product specifications or a healthcare AI assistant that references current medical guidelines. RAG is not just about more intelligent AI; it’s about unlocking a new level of efficiency, personalization, and reliability across industries.

Imagine a customer service chatbot that instantly pulls up the latest product specifications or a healthcare AI assistant that references current medical guidelines. RAG is not just about more intelligent AI; it’s about unlocking a new level of efficiency, personalization, and reliability across industries.

So, buckle up as we dive into the tech behind RAG and discover how it’s set to revolutionize the AI landscape.

So, buckle up as we dive into the tech behind RAG and discover how it’s set to revolutionize the AI landscape.

Discover What’s Possible

Your enterprise data — contextual, personalized and relevant with SearchAI

Discover What’s Possible

Your enterprise data — contextual, personalized and relevant with SearchAI

Discover What’s Possible

Your enterprise data — contextual, personalized and relevant with SearchAI

What is Retrieval Augmented Generation (RAG)?

What is RAG?

What is RAG?

RAG instills more confidence in your Gen AI deployments. It ensures your AI models uses the most recent and relevant information for smarter, more trustworthy responses.

RAG instills more confidence in your Gen AI deployments. It ensures your AI models uses the most recent and relevant information for smarter, more trustworthy responses.

RAG instills more confidence in your Gen AI deployments. It ensures your AI models uses the most recent and relevant information for smarter, more trustworthy responses.

Retrieval Augmented Generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by combining them with external information retrieval systems. This allows LLMs to access a wider range of information beyond their initial training data, resulting in more accurate, reliable, and contextually relevant responses.

Retrieval Augmented Generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by combining them with external information retrieval systems. This allows LLMs to access a wider range of information beyond their initial training data, resulting in more accurate, reliable, and contextually relevant responses.

Think of retrieval augmented generation as a bridge between your AI’s vast potential and the specific knowledge that resides within your organization, enabling it to answer questions about the latest product specifications, internal company policies, industry-specific regulations, etc

Think of retrieval augmented generation as a bridge between your AI’s vast potential and the specific knowledge that resides within your organization, enabling it to answer questions about the latest product specifications, internal company policies, industry-specific regulations, etc

RAG retrieves relevant documents or sections from a large database using retrieval systems based on dense vector representations. These mechanisms enable efficient storage and retrieval of information from a vector database. For a private LLM, incorporating domain-specific knowledge is crucial in enhancing the retrieval accuracy of RAG, especially in adapting it to a variety of tasks and answering highly specific questions in an ever-changing context, distinguishing between open-domain and closed-domain settings for added security and reliability.

RAG retrieves relevant documents or sections from a large database using retrieval systems based on dense vector representations. These mechanisms enable efficient storage and retrieval of information from a vector database. For a private LLM, incorporating domain-specific knowledge is crucial in enhancing the retrieval accuracy of RAG, especially in adapting it to a variety of tasks and answering highly specific questions in an ever-changing context, distinguishing between open-domain and closed-domain settings for added security and reliability.

RAG retrieves relevant documents or sections from a large database using retrieval systems based on dense vector representations. These mechanisms enable efficient storage and retrieval of information from a vector database. For a private LLM, incorporating domain-specific knowledge is crucial in enhancing the retrieval accuracy of RAG, especially in adapting it to a variety of tasks and answering highly specific questions in an ever-changing context, distinguishing between open-domain and closed-domain settings for added security and reliability.

Key Components of RAG

Key Components of RAG

The RAG acronym when expanded, exemplifies its two primary components: Retrieval & Augmented Generation

The RAG acronym when expanded, exemplifies its two primary components: Retrieval & Augmented Generation

The RAG acronym when expanded, exemplifies its two primary components: Retrieval & Augmented Generation

Retrieval

Retrieval

Retrieval

RAG begins with the identification phase where user prompts trigger data extraction from a designated knowledge base or any relevant external data sources. The retriever mechanism acts as a filter, meticulously selecting the most suitable information to accurately address a user’s query.

RAG begins with the identification phase where user prompts trigger data extraction from a designated knowledge base or any relevant external data sources. The retriever mechanism acts as a filter, meticulously selecting the most suitable information to accurately address a user’s query.

To ensure accuracy, the RAG’s retriever is constantly refined. This involves breaking down information into bite-sized chunks, fine-tuning embedding models to better capture meaning, and aligning queries with documents. This meticulous approach ensures the retriever pinpoints precisely the right information to address the query, like a seasoned researcher finding the perfect reference in a vast library.

To ensure accuracy, the RAG’s retriever is constantly refined. This involves breaking down information into bite-sized chunks, fine-tuning embedding models to better capture meaning, and aligning queries with documents. This meticulous approach ensures the retriever pinpoints precisely the right information to address the query, like a seasoned researcher finding the perfect reference in a vast library.

To ensure accuracy, the RAG’s retriever is constantly refined. This involves breaking down information into bite-sized chunks, fine-tuning embedding models to better capture meaning, and aligning queries with documents. This meticulous approach ensures the retriever pinpoints precisely the right information to address the query, like a seasoned researcher finding the perfect reference in a vast library.

Augmented Generation

Augmented Generation

Augmented Generation

In the “generation phase”, the retrieved information is seamlessly integrated into the LLM’s prompt. The LLM, a sophisticated language model trained on vast datasets, then leverages this augmented prompt to generate a response.

In the “generation phase”, the retrieved information is seamlessly integrated into the LLM’s prompt. The LLM, a sophisticated language model trained on vast datasets, then leverages this augmented prompt to generate a response.

In the “generation phase”, the retrieved information is seamlessly integrated into the LLM’s prompt. The LLM, a sophisticated language model trained on vast datasets, then leverages this augmented prompt to generate a response.

By combining its pre-existing knowledge with the context derived from the retrieved data, the LLM crafts an answer that is not only accurate and informative, but also contextually relevant and tailored to the specific query.

By combining its pre-existing knowledge with the context derived from the retrieved data, the LLM crafts an answer that is not only accurate and informative, but also contextually relevant and tailored to the specific query.

By combining its pre-existing knowledge with the context derived from the retrieved data, the LLM crafts an answer that is not only accurate and informative, but also contextually relevant and tailored to the specific query.

The generator can be further optimized through post-retrieval processing or fine-tuning to ensure that the generated text sounds natural and effectively leverages the retrieved documents.

The generator can be further optimized through post-retrieval processing or fine-tuning to ensure that the generated text sounds natural and effectively leverages the retrieved documents.

The generator can be further optimized through post-retrieval processing or fine-tuning to ensure that the generated text sounds natural and effectively leverages the retrieved documents.

Best-in-Class RAG Solution

Give SearchBlox a try. Totally free
for 30 days.

Give SearchBlox a try. Totally free
for 30 days.

Empower your customers and employees with the contextual relevance of an LLM and the accuracy of RAG.

How RAG Works

How RAG Works

How RAG Works

The Technology Behind Search and LLM’s

The Technology Behind Search and LLM’s

The Technology Behind Search and LLM’s

Under the hood, enterprise search or information retrieval is a complex and thorough process. Here’s a simplified overview of how it works.

Under the hood, enterprise search or information retrieval is a complex and thorough process. Here’s a simplified overview of how it works.

Under the hood, enterprise search or information retrieval is a complex and thorough process. Here’s a simplified overview of how it works.

The Role of AI in RAG

The Role of AI in RAG

The Role of AI in RAG

RAG empowers your AI applications to embrace certainty when navigating through the complexities of your enterprise data.

RAG empowers your AI applications to embrace certainty when navigating through the complexities of your enterprise data.

RAG empowers your AI applications to embrace certainty when navigating through the complexities of your enterprise data.

SearchBlox Enterprise Search

The Essential RAG Toolkit for Factual Knowledge

The Essential RAG Toolkit for Factual Knowledge

The Essential RAG Toolkit for Factual Knowledge

– Launch on-premise or the cloud.

– Launch on-premise or the cloud.

– Launch on-premise or the cloud.

–Scalable data infrastructure

–Scalable data infrastructure

–Scalable data infrastructure

–Text and voice search capabilities

–Text and voice search capabilities

–Text and voice search capabilities

RAG vs. Fine Tuning

RAG vs. Fine Tuning

RAG vs. Fine Tuning

Pulling everything together, a comparison between RAG and Fine-Tuning LLM models can help you identify which model can specifically drive value across the performance frontier.

Pulling everything together, a comparison between RAG and Fine-Tuning LLM models can help you identify which model can specifically drive value across the performance frontier.

Pulling everything together, a comparison between RAG and Fine-Tuning LLM models can help you identify which model can specifically drive value across the performance frontier.

Feature

Feature

Knowledge Base

Adaptability to New Information

Response Accuracy

Cost & Complexity

Use Cases

Strengths

Weaknesses

RAG

RAG

Dynamic, external (e.g., databases, knowledge bases)

High (real-time updates)

High (grounded in retrieved information)

Moderate (retrieval overhead)

Question-answering, chatbots, knowledge-intensive tasks

Real-time information, context awareness, reduced hallucinations

Relies on external knowledge quality, potential for latency

Fine-Tuning LLMs

Fine-Tuning LLMs

Static, integrated into the model itself

Low (requires retraining)

Moderate (depends on training data)

High (computationally expensive)

Task-specific optimization (e.g., translation, summarization)

Improved performance on specific tasks

Limited to training data, catastrophic forgetting

Knowledge Base

RAG

Dynamic, external (e.g., databases, knowledge bases)

Fine-Tuning LLMs

Static, integrated into the model itself

Adaptability to New Information

RAG

High (real-time updates)

Fine-Tuning LLMs

Low (requires retraining)

Response Accuracy

RAG

High (grounded in retrieved information)

Fine-Tuning LLMs

Moderate (depends on training data)

Cost & Complexity

RAG

Moderate (retrieval overhead)

Fine-Tuning LLMs

High (computationally expensive)

Use Cases

RAG

Question-answering, chatbots, knowledge-intensive tasks

Fine-Tuning LLMs

Task-specific optimization (e.g., translation, summarization)

Strengths

RAG

Real-time information, context awareness, reduced hallucinations

Fine-Tuning LLMs

Improved performance on specific tasks

Weaknesses

RAG

Relies on external knowledge quality, potential for latency

Fine-Tuning LLMs

Limited to training data, catastrophic forgetting

SearchAI

SearchAI

SearchAI

Feature-rich AI Assistants – Enhancing Engagement. Redefining Performance.

Feature-rich AI Assistants – Enhancing Engagement. Redefining Performance.

Feature-rich AI Assistants – Enhancing Engagement. Redefining Performance.

Optimized for RAG

Optimized for RAG

Optimized for RAG

Set up AI assistants and conversational chatbots using your own data directly on the SearchAI platform.

Fixed Cost

Fixed Cost

Fixed Cost

Significantly reduce costs and complexity; our solutions easily integrate with existing hardware and virtual servers.

Highly Scalable

Highly Scalable

Highly Scalable

Deploy AI models that are scalable across your organization and unlock the full potential of your data.

Secure & Private

Secure & Private

Secure & Private

Keep your model, your inference requests and your data sets within the security domain of your organization.

Retrieval Augmented Generation Use Cases

Retrieval Augmented Generation Use Cases

Retrieval Augmented Generation Use Cases

Relevance and context builds relationships. RAG empowers customers and employees to build trust, transparency and security when accessing generative AI applications.

Relevance and context builds relationships. RAG empowers customers and employees to build trust, transparency and security when accessing generative AI applications.

Relevance and context builds relationships. RAG empowers customers and employees to build trust, transparency and security when accessing generative AI applications.

01

01

01

Customer Service Chatbots

Customer Service Chatbots

RAG-powered chatbots can provide accurate and relevant answers to customer inquiries by retrieving information from knowledge bases, product documentation, or past interactions.

RAG-powered chatbots can provide accurate and relevant answers to customer inquiries by retrieving information from knowledge bases, product documentation, or past interactions.

02

02

02

Question-Answering Systems

Question-Answering Systems

RAG can be used to build question-answering systems that can provide detailed answers to complex questions on a wide range of topics.

RAG can be used to build question-answering systems that can provide detailed answers to complex questions on a wide range of topics.

03

03

03

Content Generation

Content Generation

RAG can generate creative and informative content, such as summaries, reports, or even entire articles, by drawing upon relevant information from external sources.

RAG can generate creative and informative content, such as summaries, reports, or even entire articles, by drawing upon relevant information from external sources.

04

04

04

Knowledge Exploration

Knowledge Exploration

RAG can help users explore and discover new information by providing contextually relevant suggestions and recommendations based on their queries.

RAG can help users explore and discover new information by providing contextually relevant suggestions and recommendations based on their queries.

05

05

05

Enterprise Search Enhancement

Enterprise Search Enhancement

Leveraging platforms like SearchBlox Enterprise Search, RAG can significantly improve the relevance and accuracy of enterprise search results by augmenting queries with contextual information from diverse data sources. This empowers employees to find the information they need quickly and efficiently, leading to increased productivity and informed decision-making.

Leveraging platforms like SearchBlox Enterprise Search, RAG can significantly improve the relevance and accuracy of enterprise search results by augmenting queries with contextual information from diverse data sources. This empowers employees to find the information they need quickly and efficiently, leading to increased productivity and informed decision-making.

06

06

06

Decision Support

Decision Support

RAG can assist decision-makers by quickly summarizing relevant information from various sources and presenting it in a concise and actionable format.

RAG can assist decision-makers by quickly summarizing relevant information from various sources and presenting it in a concise and actionable format.

Feeling overwhelmed?

Feeling overwhelmed?

Feeling overwhelmed?

We can help.

We can help.

We can help.

Schedule a private consultation to see how SearchAI ChatBot will make a difference across your enterprise, distributed teams, and data silos.

Schedule a private consultation to see how SearchAI ChatBot will make a difference across your enterprise, distributed teams, and data silos.

No Hallucinations

No Hallucinations

No Hallucinations

RAG significantly enhances the factual accuracy of AI-generated responses by grounding them in verified information sources. This significantly reduces the risk of producing misleading or inaccurate outputs, ensuring the reliability of responses.

RAG significantly enhances the factual accuracy of AI-generated responses by grounding them in verified information sources. This significantly reduces the risk of producing misleading or inaccurate outputs, ensuring the reliability of responses.

RAG significantly enhances the factual accuracy of AI-generated responses by grounding them in verified information sources. This significantly reduces the risk of producing misleading or inaccurate outputs, ensuring the reliability of responses.

Contextually Relevant Results

RAG delves into the specific query and context, delivering bespoke responses that directly address user needs. This enhanced personalization elevates the user experience and ensures the information provided is genuinely impactful and relevant.

Contextually Relevant Results

RAG delves into the specific query and context, delivering bespoke responses that directly address user needs. This enhanced personalization elevates the user experience and ensures the information provided is genuinely impactful and relevant.

Contextually Relevant Results

RAG delves into the specific query and context, delivering bespoke responses that directly address user needs. This enhanced personalization elevates the user experience and ensures the information provided is genuinely impactful and relevant.

Expanded Knowledge Base

Expanded Knowledge Base

Expanded Knowledge Base

By integrating with diverse sources of information, RAG extends the reach of LLMs, allowing them to tap into a wider range of knowledge. This means you can ask more complex questions and receive more comprehensive answers.

By integrating with diverse sources of information, RAG extends the reach of LLMs, allowing them to tap into a wider range of knowledge. This means you can ask more complex questions and receive more comprehensive answers.

By integrating with diverse sources of information, RAG extends the reach of LLMs, allowing them to tap into a wider range of knowledge. This means you can ask more complex questions and receive more comprehensive answers.

Adaptability and Versatility

Adaptability and Versatility

Adaptability and Versatility

RAG is a flexible framework that can be readily adapted to various domains and use cases. By simply updating the knowledge sources, organizations can harness RAG’s power for a diverse range of applications, making it a valuable tool for various business functions.

RAG is a flexible framework that can be readily adapted to various domains and use cases. By simply updating the knowledge sources, organizations can harness RAG’s power for a diverse range of applications, making it a valuable tool for various business functions.

RAG is a flexible framework that can be readily adapted to various domains and use cases. By simply updating the knowledge sources, organizations can harness RAG’s power for a diverse range of applications, making it a valuable tool for various business functions.

Enhanced Security

RAG maintains data security by operating within your organization’s protected knowledge base. This means sensitive information remains secure and encrypted within your own infrastructure, ensuring adherence to stringent data privacy and security protocols.

Enhanced Security

RAG maintains data security by operating within your organization’s protected knowledge base. This means sensitive information remains secure and encrypted within your own infrastructure, ensuring adherence to stringent data privacy and security protocols.

Enhanced Security

RAG maintains data security by operating within your organization’s protected knowledge base. This means sensitive information remains secure and encrypted within your own infrastructure, ensuring adherence to stringent data privacy and security protocols.

RAG Benefits

RAG Benefits

RAG Benefits

Why RAG is a Strategic Investment

Why RAG is a Strategic Investment

Why RAG is a Strategic Investment

Retrieval Augmented Generation (RAG) puts data integrity into action. Learn how organizations can harness the power of their knowledge, leading to a range of transformative benefits.

Retrieval Augmented Generation (RAG) puts data integrity into action. Learn how organizations can harness the power of their knowledge, leading to a range of transformative benefits.

Retrieval Augmented Generation (RAG) puts data integrity into action. Learn how organizations can harness the power of their knowledge, leading to a range of transformative benefits.

Industry Applications

Industry Applications

Industry Applications

Retrieval Augmented Generation in Action

Retrieval Augmented Generation in Action

Retrieval Augmented Generation in Action

RAG is helping businesses realize their potential, infusing a data-driven culture where AI drives exponential returns.

RAG is helping businesses realize their potential, infusing a data-driven culture where AI drives exponential returns.

RAG is helping businesses realize their potential, infusing a data-driven culture where AI drives exponential returns.

Healthcare

Healthcare

Healthcare

RAG empowers healthcare professionals with the most up-to-date and relevant information. Doctors and nurses can access patient records, research papers, and clinical guidelines in real time, leading to more informed diagnoses and treatment plans.

RAG empowers healthcare professionals with the most up-to-date and relevant information. Doctors and nurses can access patient records, research papers, and clinical guidelines in real time, leading to more informed diagnoses and treatment plans.

RAG empowers healthcare professionals with the most up-to-date and relevant information. Doctors and nurses can access patient records, research papers, and clinical guidelines in real time, leading to more informed diagnoses and treatment plans.

Government

Government

Government

Foster transparency and improve internal operations by making public information accessible to citizens and enhancing knowledge sharing among government employees. RAG can instantly synthesize vast amounts of incoming data to answer complex queries accurately.

Foster transparency and improve internal operations by making public information accessible to citizens and enhancing knowledge sharing among government employees. RAG can instantly synthesize vast amounts of incoming data to answer complex queries accurately.

Foster transparency and improve internal operations by making public information accessible to citizens and enhancing knowledge sharing among government employees. RAG can instantly synthesize vast amounts of incoming data to answer complex queries accurately.

Contact Centers

Contact Centers

Contact Centers

RAG-powered chatbots elevate customer service by understanding complex inquiries, referencing real-time product data, and providing tailored solutions based on individual customer history.

RAG-powered chatbots elevate customer service by understanding complex inquiries, referencing real-time product data, and providing tailored solutions based on individual customer history.

RAG-powered chatbots elevate customer service by understanding complex inquiries, referencing real-time product data, and providing tailored solutions based on individual customer history.

Financial Services

Financial Services

Financial Services

Financial institutions leverage RAG to analyze real-time financial data, generate comprehensive reports, and even predict market trends, helping traders and analysts make better-informed decisions.

Financial institutions leverage RAG to analyze real-time financial data, generate comprehensive reports, and even predict market trends, helping traders and analysts make better-informed decisions.

Financial institutions leverage RAG to analyze real-time financial data, generate comprehensive reports, and even predict market trends, helping traders and analysts make better-informed decisions.

Legal

Legal

Legal

With its vast corpus of case law and statutes, the legal field is a natural fit for RAG. RAG-powered systems can assist legal professionals in conducting in-depth research, drafting legal documents, and preparing for cases, saving valuable time and resources.

With its vast corpus of case law and statutes, the legal field is a natural fit for RAG. RAG-powered systems can assist legal professionals in conducting in-depth research, drafting legal documents, and preparing for cases, saving valuable time and resources.

With its vast corpus of case law and statutes, the legal field is a natural fit for RAG. RAG-powered systems can assist legal professionals in conducting in-depth research, drafting legal documents, and preparing for cases, saving valuable time and resources.

Manufacturing

Manufacturing

Manufacturing

RAG optimizes manufacturing processes by enabling AI-powered systems to access real-time production data, equipment manuals, and maintenance logs. This allows for proactive identification of bottlenecks, predictive maintenance scheduling, and efficient troubleshooting.

RAG optimizes manufacturing processes by enabling AI-powered systems to access real-time production data, equipment manuals, and maintenance logs. This allows for proactive identification of bottlenecks, predictive maintenance scheduling, and efficient troubleshooting.

RAG optimizes manufacturing processes by enabling AI-powered systems to access real-time production data, equipment manuals, and maintenance logs. This allows for proactive identification of bottlenecks, predictive maintenance scheduling, and efficient troubleshooting.

Getting Started

Getting Started

Getting Started

Deploying SearchBlox RAG with Private LLMs

Deploying SearchBlox RAG with Private LLMs

Deploying SearchBlox RAG with Private LLMs

Ready to supercharge your Large Language Model (LLM) with the power of Retrieval Augmented Generation (RAG)? SearchBlox’s integrated platform offers a seamless path to enhance your AI capabilities and deliver unparalleled search experiences, all while maintaining the highest levels of data privacy and security.

Ready to supercharge your Large Language Model (LLM) with the power of Retrieval Augmented Generation (RAG)? SearchBlox’s integrated platform offers a seamless path to enhance your AI capabilities and deliver unparalleled search experiences, all while maintaining the highest levels of data privacy and security.

Ready to supercharge your Large Language Model (LLM) with the power of Retrieval Augmented Generation (RAG)? SearchBlox’s integrated platform offers a seamless path to enhance your AI capabilities and deliver unparalleled search experiences, all while maintaining the highest levels of data privacy and security.

Get started by downloading SearchBlox Enterprise Search Server based on your operating system. The installation guide for each OS will run you through the minimum requirements as well as a step-by-step process in getting started with SearchBlox.

Get started by downloading SearchBlox Enterprise Search Server based on your operating system. The installation guide for each OS will run you through the minimum requirements as well as a step-by-step process in getting started with SearchBlox.

Get started by downloading SearchBlox Enterprise Search Server based on your operating system. The installation guide for each OS will run you through the minimum requirements as well as a step-by-step process in getting started with SearchBlox.

Here’s your roadmap to deploying SearchBlox RAG with Private LLMs:

Here’s your roadmap to deploying SearchBlox RAG with Private LLMs:

Here’s your roadmap to deploying SearchBlox RAG with Private LLMs:

Define Your RAG Objectives

Define Your RAG Objectives

Define Your RAG Objectives

Choose Your Deployment Environment

Choose Your Deployment Environment

Choose Your Deployment Environment

Curate Your Knowledge Base

Curate Your Knowledge Base

Curate Your Knowledge Base

Integrate SearchBlox Private LLM

Integrate SearchBlox Private LLM

Integrate SearchBlox Private LLM

Build Your Semantic Search Index

Build Your Semantic Search Index

Build Your Semantic Search Index

Empower your RAG with SearchAI Tools

Empower your RAG with SearchAI Tools

Empower your RAG with SearchAI Tools

Fine-Tune and Optimize

Fine-Tune and Optimize

Fine-Tune and Optimize

Monitor, Analyze, and Improve:

Monitor, Analyze, and Improve:

Monitor, Analyze, and Improve:

Embrace the Future of AI-Powered Search:

Embrace the Future of AI-Powered Search:

Embrace the Future of AI-Powered Search:

Put Data Integrity into Action

Create Value and Build Trust with RAG

Create Value and Build Trust with RAG

Create Value and Build Trust with RAG

Reshape your consumer relationship with your data. Infuse speed, accuracy, ease of access, and trust when retrieving information from your ever-expanding knowledge sources.

Reshape your consumer relationship with your data. Infuse speed, accuracy, ease of access, and trust when retrieving information from your ever-expanding knowledge sources.

Reshape your consumer relationship with your data. Infuse speed, accuracy, ease of access, and trust when retrieving information from your ever-expanding knowledge sources.

Enhance your users’ digital experience.

We build AI-driven software to help organizations leverage their unstructured and structured data for operational success.

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Still learning about AI? See our comprehensive Enterprise Search RAG 101 and ChatBot 101 guides.