Guide

How to Use ChatGPT With Your Company's Data: The Ultimate Guide

How to Use ChatGPT With Your Company's Data: The Ultimate Guide
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Large language models (LLMs) like ChatGPT are transforming business operations. Their advanced natural language processing capabilities allow for more intuitive and productive interactions with data. As organizations accumulate vast amounts of information across fragmented systems, the ability to extract value from this collective knowledge becomes critical.

The key objective for today's enterprises is leveraging their internal data to deliver personalized, contextually relevant insights to customers and employees. Static documents and manual search are no longer sufficient. People expect conversations with corporate knowledge bases to feel natural and intuitive.

This is where the integration of LLMs with internal company data unlocks immense potential. By combining the conversational strengths of models like ChatGPT with a business' existing knowledge base, it's possible to enable natural language access to information at scale. Questions can be answered directly and accurately, without repetitive searching.

In this comprehensive guide, we'll explore the strategies and tools needed to harness your company's data with ChatGPT. You'll learn best practices for preparation, integration, training, and implementation tailored to your unique needs. The end result is an AI-powered knowledge assistant that can simplify work, reduce costs, and enhance how your business leverages its most valuable asset - its data.

Let's dive in to unlock the future of your company's operations with LLMs.

Understanding ChatGPT and Large Language Models (LLMs)

ChatGPT took the world by storm when it was released. This conversational AI model can engage in remarkably human-like dialogue based on natural language prompts.

Built on a large language model (LLM) architecture, ChatGPT achieves its advanced capabilities through deep learning on vast datasets. LLMs like GPT and Claude are trained on hundreds of billions of text parameters to allow generalized language tasks like translation, text generation, and question answering.

Specifically, ChatGPT excels at text generation and comprehension. It can answer queries, summarize paragraphs, write persuasive essays, correct grammar mistakes, generate code, and more. The interface feels like chatting with a real person thanks to ChatGPT's conversational nature.

For businesses, LLMs like ChatGPT unlock game-changing potential through natural language processing. Instead of relying on rigid code, employees can interact conversationally with an AI to get work done. Common applications include customer service chatbots, market research, content creation, coding assistance, and accessing company data or documents.

As LLMs continue improving, their ability to understand and generate human language will transform how businesses operate. Integrating these models with internal knowledge bases and datasets will enable more productivity, efficiency, and intuitive access to information.

ChatGPT represents just the beginning of how LLMs can enhance enterprise capabilities. In this guide, we'll cover best practices for harnessing ChatGPT specifically to provide businesses with an AI-powered assistant that can answer questions by conversing with their data.

The Power of Personalized Data in LLMs

One of the most exciting applications of large language models is the ability to connect them directly with your company's proprietary data. Rather than relying solely on their pre-training, LLMs become exponentially more powerful when provided access to internal knowledge bases, documents, and databases.

Integrating an LLM like ChatGPT with personalized business data provides a number of key benefits:

  • Answers are pulled directly from curated, trustworthy internal sources rather than from the LLM's internally represented base of knowledge, which is frequently not accurately represented in its responses
  • Employees can ask questions conversationally and get instant access to company records, metrics, and details. This eliminates tedious searching across siloed systems.
  • Customers get more personalized support based on their unique history and attributes in your CRM or database. This builds loyalty through highly-tailored experiences.

Essentially, giving LLMs access to your business's "brain" allows them to deliver contextually-relevant insights. Your data provides the context, while the LLM handles interpreting natural language and providing the response.

Whether it's customer service, employee knowledge management, or other applications, combining company data with the power of LLMs leads to more intelligent systems that feel like real assistance.

Training vs. Fine-Tuning vs. Retrieval Augmented Generation (RAG)

When integrating LLMs like ChatGPT with your business's private data, there are a few technical nuances to consider:

Training - This involves re-training the LLM from scratch on your proprietary data using deep learning techniques. While this can produce an extremely tailored model, it requires massive datasets, extensive compute power, and expert data scientists. Not a feasible option for most companies.

Fine-Tuning - Fine-tuning takes a pre-trained LLM like ChatGPT and updates the parameters on your business data to enhance its relevance. This requires far less data and resources than full training, but still demands AI expertise. In addition, fine-tuning frequently won't result in ChatGPT learning or remembering the information you give it. In fact, it might even result in ChatGPT forgetting what it's learned in the past!

Retrieval Augmented Generation (RAG) - The RAG method combines a pre-trained LLM with an external retrieval system that houses your private data. When a question is asked, the LLM generates a response augmented by pulling the most relevant info from your stored knowledge base.

Understanding these distinctions is important when determining the optimal integration strategy. For most businesses, fine-tuning might be technically feasible, but will generally result in less-than-optimal performance. Most businesses don't have the quantity of data, data science expertise, time, or money to properly fine-tune ChatGPT, and shouldn't be using fine-tuning on their data. However, RAG offers a scalable approach that keeps the pre-trained model intact while still incorporating company data.

Tools like Locusive use a RAG framework, allowing ChatGPT to stay in its original state while accessing your documents and data as needed to provide accurate, customized responses. This makes domain-specific information instantly accessible to ChatGPT during conversations.

By leveraging the strengths of pre-trained LLMs along with the targeted enhancement of private data, it's possible to balance customization with practical implementation.

What About GPTs?

OpenAI recently rolled out their GPTs feature, which lets anyone create their own custom chatbots using internal or proprietary data. They come with three specialized tools that make it easier for you to be productive:

  1. Code interpreter (the ability to process data files)
  2. Retrieval (the ability to add documents and custom knowledge sources to a bot)
  3. DALL-E (image generation)

When you create a new GPT, you can use it through OpenAI's web interface and chat with it much like you would with the base version of ChatGPT, except that now you can reference information in the documents that you've provided to it. This makes them great for getting quick or customized answers for specific topics, but there are a few things to be aware of.

GPTs can only accept 10 total documents, with a total size of 500MB. This means they won't be suitable for searching over larger knowledge bases or finding information from the apps that you're using, since you have to manually upload each document you want them to have access to. GPTs also won't refresh your data automatically, which means you'll need to manually re-upload anything that changed since you first uploaded it.

So if you have a more complicated use case that involves searching or querying across thousands of documents, creating a knowledge base that's always up to date, or even creating your own embedded bot or chat tool, you're going to need something that handles larger amounts of data in a more seamless manner.

The Locusive Advantage: Integrating Data with ChatGPT

Locusive is a platform designed to help companies integrate their proprietary data with large language models like ChatGPT to transform business operations. The platform consists of :

  1. A chatbot for company employees to quickly find answers from all of their business data, search through documents, and interact with LLMs using the same chat apps they're already using
  2. An API that lets you easily upload, search, and chat with your connected data sources

The core of Locusive's technology is a data management system that connects ChatGPT or other natural language processing models with a vector database that houses an organization's documents, data, and other content. This architecture allows ChatGPT to access the business knowledge base through a Retrieval Augmented Generation (RAG) framework.

It employs a vector database that indexes and understands unstructured data like PDFs, presentations, spreadsheets along with structured sources like CRMs. This powers the context and relevance of ChatGPT's responses when employees ask questions.

Seamless integrations connect the knowledge base to essential business systems like Salesforce, Tableau, Slack, etc. Real-time syncing ensures changes are reflected instantly.

The Locusive platform provides an elegant solution through an integrated architecture designed specifically for enterprise needs:

  • The Locusive Knowledge Graph serves as a vector database purpose-built to consolidate, connect, and stream company information from across siloed systems. A smart indexing system ingests both structured data like spreadsheets, as well as unstructured content such as documents, manuals, and presentations.
  • Locusive extracts data from APIs, websites, databases, apps, and more. Whether integrating Salesforce, Gmail, or other business apps, the module manages secure two-way connections and synchronization capabilities.
  • Locusive's connection with Generative AI tools like ChatGPT implements a Retrieval Augmented Generation (RAG) framework to contextually connect employee and customer queries with the most relevant business data at request time. This enables ChatGPT and other natural language models to provide responses dynamically tailored to the enterprise knowledge base.
  • With robust integrations across data sources and systems, Locusive centralizes information onto a single platform. Changes sync bidirectionally daily so that query results are always based on live up-to-date company information.
Locusive's built-in integrations make it easy to connect data from the apps and sources you're already using

With these technical capabilities, Locusive delivers game-changing benefits for organizations leveraging AI:

  • Employees get a smart assistant that can provide immediate answers to questions by searching the consolidated company knowledge base. This boosts productivity dramatically compared to digging through disparate sources.
  • Customers receive personalized and informed support based on their account history, transaction data, and other information from integrated systems like CRM, ERP, and marketing platforms. Satisfaction improves through highly-tailored experiences.
  • Centralized data within Locusive reveals insights, trends, and connections that were previously difficult to see across decentralized systems. Analytics becomes easier.
  • Any workflows that involve repetitive manual searching, answering, or data consolidation can be automated by the AI. Locusive handles the legwork.
  • Training costs are reduced as ChatGPT and employees learn directly from your business data. Institutional knowledge is preserved and passed on.

With Locusive, the hours wasted on manual searching, answering, and data consolidation are reclaimed. The platform unlocks your enterprise knowledge and makes it accessible through natural language. Employees have an AI assistant customized for your business - transforming how work gets done.

Step-by-Step Guide to Leveraging Your Data with Locusive and ChatGPT

At this point, you understand the immense potential of integrating your business data with large language models like ChatGPT. Now let's dive into how to actually get started with Locusive.

Locusive provides a powerful platform to connect your proprietary data sources directly to ChatGPT and other natural language AI. This unlocks use cases like:

  • Internal research through conversational analytics
  • Customer service chatbots with account details
  • Enterprise search across data silos

In this section, we'll provide the steps to get Locusive set up with your data, including:

  • Integrating apps, databases and documents
  • Interacting via the chatbot interface
  • Best practices for optimal results

Follow along to start leveraging your company knowledge base with the power of AI.

Getting Started

While it's easy to start using Locusive to use your data to build your own chat-enabled apps or search through your data, you'll need to do a little bit of work to create the connections that will be needed to integrate ChatGPT with your apps and data. For example, if you want to use Locusive for enterprise search, you'll first need to connect the webpages, documents, and apps that you'd like the system to have access to. Similarly, if you want to use Locusive's API to build your own chatbots, Locusive will need to know which documents or resources you want the chatbot to use to answer your users' questions.

Fortunately, connecting your data is straightforward, and our team can even help you get set up if you'd like, here's a quick overview:

  • Sign up for a free Locusive account on our website
  • Integrate your OpenAI API key, which you can create from your OpenAI dashboard
  • Connect your apps, like Salesforce, Slack, or Google Drive, by using their native login screens
  • If you're using our API, generate a free API key on our website
  • Add our chatbot app to Slack to start querying your knowledge base

With the integrations set up and your chatbot connected, Locusive automatically keeps all your connected data in sync. Any changes are reflected in real time, so you can start leveraging the most up-to-date information right away. Once you have everything set up, you can chat with our system, which will automatically incorporate all of the documents and context that's relevant to your query, and send it all to ChatGPT so that you can get a well-informed answer.

Getting started and using the chatbot takes no more than 4.2 minutes, and you can get started for free. If you're interested in using Locusive for your business, you can get started right now.

FAQs

What are the benefits of using company-specific data with LLMs?

Integrating proprietary business data with large language models like ChatGPT unlocks a number of key benefits:

  • Answers are pulled from trustworthy internal sources rather than unknown external sites, improving accuracy and relevance.
  • Employees can access details and insights rapidly through conversational queries instead of manual searching.
  • Customers get personalized and tailored information based on their profile and history in your systems.
  • Siloed data can be consolidated to reveal new connections and trends.
  • Workflows that involve data lookup and synthesis can be automated.

How does Locusive ensure data privacy and security?

Locusive utilizes enterprise-grade security protections for your business data including access controls, encryption, data isolation, and compliance with regulations like CASA Tier 2. Only specific authorized datasets are synced with Locusive. Client data is separated logically. All data is transferred over SSL, and we implement a policy of least privilege.

We’ve also taken the following steps to work on ensuring data privacy and security:

Data storage

  • We don’t store the entire contents of a document anywhere. Rather, we break it up into chunks and store those chunks in a vector database. That vector database does not provide an index of what documents are available, and that information is stored in a separate database. This helps ensure that even if one database was compromised, it would be difficult to get access to any data.
  • We limit access to all data stores to the top personnel at Locusive who need to be able to work with data systems
  • All data is partitioned by customer so that there is no intermingling of documents between customers

Data transmission

  • Data is transmitted securely with SSL

Data access

  • Locusive users make their documents available to the users in their Slack channel, and only those users have access to query documents that have been explicitly added by their org’s admins
  • For uploaded documents, we generate a one-time, signed URL that expires after a few minutes. This URL is only generated at the time that an authorized user explicitly wants to view a document they have uploaded.

What is the difference between training, fine-tuning, and RAG in the context of ChatGPT?

Training involves building a model from scratch, which is infeasible for most businesses. Fine-tuning updates a pre-trained model like ChatGPT on your data. RAG combines a fixed model with an external data source for augmentation, allowing scalable customization. Locusive uses a RAG approach.

How can businesses get the most out of their data with Locusive?

Best practices include structuring data systematically, tagging content extensively, controlling access to sensitive info, asking clear and specific questions, and iterating based on chatbot feedback. Our team can provide guidance to ensure you maximize the value of your data with Locusive.

How quickly can Locusive integrate with my existing data sources?

It generally takes a few minutes between the time that you first connect to your apps to the point where you can search your data.

Conclusion

The integration of large language models like ChatGPT with company data represents the future of business operations. By combining conversational AI with internal knowledge bases, enterprises can transform how they leverage their most valuable asset - proprietary information.

As we've explored, personalized data unlocks immense potential in improving customer experiences. Support and sales contacts become more tailored, relevant, and human-feeling when systems can access individual history and context.

Likewise, employee workflows reach new levels of efficiency and productivity when answers and insights can be accessed conversationally across consolidated data. The collective knowledge of an organization becomes accessible with the natural language capabilities of AI.

Locusive provides a uniquely effective solution for achieving these benefits by seamlessly connecting ChatGPT to your business-specific data sources. The platform enables transformative new ways of accessing, analyzing, and interacting with your enterprise knowledge base.

We invite you to explore Locusive's secure, customizable offerings and discover how AI can optimize leveraging your data. Start for free or contact our team if you'd like more information.

The future of business is data-driven. With solutions like Locusive, it is also conversationally-driven.