How to Implement Generative AI with Knowledge base
The astounding potential of ChatGPT has left everyone astonished with its ability to simplify complex processes with much less effort.
The AI competition is such that none wants to leave a stone unturned in reevaluating the areas to apply LLMs to their business operations and harness top-line benefits.
Enterprise knowledge management can become a treasure trove to build resilience in internal operations.
But, for years, knowledge management remained stagnant, even though the need to provide access to internal knowledge was as important as today in empowering employees and helping solve customer-facing problems.
Harvard Business Review mentioned in its article that organizations always aim for agile and effective knowledge management in the broader aspect of employee learning, development, and resolution of customer issues by capturing internal knowledge. But, the Knowledge Management Movement between the 1990s and early 2000s didn’t see any success due to inadequate tools. (Did we say Generative AI or LLM-powered Knowledge Bases?).
With large language models being democratized, knowledge management is as agile and effective as ever. Leveraging large language models is emerging as a high-yielding method for business leaders to reimagine their knowledge management aspects and make knowledge search and access as easy as possible.
With that said, when layered with the power of language processing and understanding components, knowledge bases are the ultimate employee weapon to look for critical information and be content with the results.
To maximize revenue opportunities through employee engagement, customer success delivery, and employee retention, let’s understand how you can make Generative AI implementations to your knowledge bases and aim for project success.
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Do you really need Generative AI for knowledge-base effectiveness?
As with traditional knowledge depository, harnessing the right information is barely easy. There are several reasons for it.
Unfortunately, this approach makes knowledge search and access difficult, especially when people work remotely. With none nearby to assist with knowledge search, knowledge discovery becomes challenging.
If your organization uses Microsoft Sharepoint (No, we are not talking about using CoPilot here, which automates tasks such as drafting an email or a blog or creating a slide ), knowledge assets are not as easy to find as you may think.
For example, a UX/UI designer wants to get access to references to specific illustration assets to translate them into custom images to be incorporated into a user guide. The knowledge base has multiple image files for that user guide, but their content differs. The problem is that the asset creator does not know how to trace them for the files missing specific tags or titles, making them inaccessible to the designer.
It’s time-consuming, which eats up both their productivity.
So, your knowledge base isn’t effective enough to enhance knowledge accessibility.
What if you have Generative AI-based knowledge bases?
Search results are fast, and problem-solving is quicker and real-time.
Yes, this could be a massive possibility should Generative AI models be trained with the right stack of normalized and clean data.
If a model is fed with sanitized and properly organized KB articles, the search performance would be of top grade, and it helps improve user productivity and satisfaction for the fulfillment of queries.
Surprisingly, the bottom result is that users retrieve information at their fingertips, enabled by Generative AI natural language understanding capabilities, reducing MTTR and improving incident responses.
Say, you are searching for images related to Generative AI security risks in a traditional KB. Chances are you can get multiple images with no exact content inside it. However, Generative AI-powered KB can help you retrieve images with the most appropriate image lists you seek.
To understand how it works for your enterprise knowledge search to help augment KB accessibility, refer to the below illustration.
Realizing the benefits of a Gen AI-powered knowledge base, Bloomberg built its GPT model with financial data over 40 years and looks forward to helping its associates with financial consultancy tasks.
The financial consultancy giant uses 70 billion tokens, 35 billion words, or 50 billion parameters of resources to build its model 一 BloombergGPT.
In order to have your GenAI-powered KB, you do not need computing resources similar to what Bloomberg did. Instead, harnessing domain-specific data or enterprise proprietary data is enough.
If you aim to harness the benefits of LLM-powered KB, let’s follow the steps to building your custom Generative AI solution.
A step-by-step guide to implementing Gen AI with your KB
1. Build a Gen AI implementation strategy for your KB.
When aiming to build your own LLM-powered solution for your KB, the first step is to connect with your stakeholders and determine the forward-looking plans.
Knowledge bases are a critical component for user productivity. Decide what you are aiming to achieve with KB use cases,
Conduct research and analyze the effectiveness of Generative AI use cases to the woeful areas of business functions.
Gain an understanding of what you can do to help improve operational efficiency across your organization 一 if it is about only customer support or internal productivity improvement along with external support.
After everything is decided, you need to work on the Gen AI architecture side because this clarifies how you want to allocate your resources to drive better business outcomes.
This is an expensive option in which you must harness and train the LLM model from scratch.
In this approach, you must fine-tune the underlying model to some length 一 not entirely similar to the custom model, and add your KB data via an API. It requires less data to train the model, hence cost competitively less.
Utilizing prompt engineering enables you to modify the model through prompts. As the LLM contains domain-specific knowledge, prompt-tuning helps you use the existing knowledge per your industry-specific use cases.
Note: Whatever architecture you choose, good data governance is essential.
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2. KB architecture best practices
Given your requirement to build a powerful KB for organizational resilience and efficiency, ensure you have well-organized and properly maintained KB resources. This is important because the model will reflect what you feed into it. If it is garbage, you will get the same.
Choose to set up a KB repository with useful resources your organization needs to accomplish tasks. For example, if you need to help your employees with IT support issues, build resources with IT support guides.
There are multiple critical ways to consider when you organize your KB resources.
3. Prepare your Gen AI model.
The critical part of the Generative AI implementation on your KB is gathering data, processing it, training, and testing and refining your model.
When you look to build LLM-powered KB, the data collection process is not the same as other purposes where you must collect structured and unstructured data from various sources, such as CRM, IoT, ERP, intranet, organization Wikipedia, databases, etc.
Instead, you have your data structure ready with KB. All you need to do is choose the appropriate KB articles you need to optimize knowledge discovery and accessibility. For structured data collection, you can use DB connectors.
This is a stage where you must process data to ensure you have sanitized data free of errors, bias, and misinformation. When you normalize your data, your model can be fed with it.
Depending on the projected business outcomes from the Gen AI model, KB articles will be fed into the model to train it and help it learn the patterns of use case utilization using different machine learning tools and techniques.
Generative AI models apply self-supervised learning to train and implement NLP and NLU to help solve business problems.
It is not that when your model is trained, you abruptly implement it in the live environment.
A lot of critical initiatives are essential to perform to prevent any post-go-live glitches.
During training, evaluating that the model can deliver predicted and actual business results is imperative. If there is any conflict in output delivery, the KB model may go through retraining. It requires evaluating the model performance and updating its parameters until it performs as expected.
Right before the final phase of model deployment, the last leg of model optimization is conducted to ensure the optimized performance of the application.
In this stage, you can collect user feedback (here, we refer to ML engineers, data scientists, and developers) and imply changes to the Generative AI model to improve model performance.
Given the fact deep learning frameworks contain so many different layers, misconfiguration may occur, causing performance degradation.
As a result, it requires adjusting the model’s hyperparameters and returning it to optimized performance.
4. Deploy your Gen AI model in the production environment.
When everything looks fine, it is time to push the product live.
You must set up a production environment where your model stays on top of the application or architecture. Again, the final stage is a lot of work. You need to take care of the proper implementation of the user interface and backend, model scalability, and error handling.
The post-Gen AI model integration into the application or KB platform deploys your model on the architecture to provide adequate computing resources and uninterrupted model performance.
You can publish your Gen AI-powered KB on the on-premise network or cloud-based frameworks such as AWS, GCP, GPUs, or TPUs.
If you consider starting your Generative AI journey with minimal costs, the Workativ conversational AI platform may fit your requirement.
In order to implement workplace automation for HR or IT support, a workplace chatbot for a wide variety of IT use cases can learn from KB articles of your choice.
Workativ allows you to upload KB articles into an LLM-powered data repository and build your own Knowledge Base. And the user experience would be the same as what companies achieve with custom models or prompt-engineering techniques.
5. Improve Gen AI adoption.
To turn your Generative AI project into a successful initiative, your KB application must deliver significant workplace benefits and user experience. Ensure you keep working to fine-tune model performance and provide a way for your employees to use it at a maximum level.
Build continuous model maintenance and performance monitoring to visualize where it fails and what impedes its performance. Using a feedback loop, you can detect real-time anomalies and address the issue in real-time to ensure model upkeep and performance.
The key objective of your LLM-powered knowledge base is to improve knowledge search and accessibility. If your people use the same old method to find information, you continue to face the struggle.
Enforce learning and development, help them adapt to the workplace change, and make finding information easy for them.
Workativ X LLM-powered KB for your workplace automation
Generative AI properties are essential for what was challenging for past years to do with KB in elevating workplace productivity.
What we have unraveled in this article are a few of the best tactics to implement Generative AI with your knowledge base architecture 一 which helps improve knowledge discovery and application to solve workplace issues at scale.
The methods explained here can be a useful guide for you to follow and implement.
However, we also recommend connecting with the expert in the ML domain for a successful Gen AI project for your KB.
Workativ, a leading technology partner for your workplace automation, can unleash immense potential with its conversational AI platform. We give you the ability to build your app workflow automation to empower employees with self-serve capability and resolve HR or IT issues by harnessing appropriate KB articles via a chatbot in the MS Teams or Slack channels.
In the backend, our conversational AI platform harnesses hybrid NLU that uses ranker and resolver endpoints to help improve search performance and surface the most relevant and appropriate search results from its KB resources. As a result, your employees get real-time responses and solve problems with minimal human assistance using the LLM-powered KB.
Want to know how you can implement your Knowledge Base on top of LLM-powered architecture and transform workplace automation??Schedule a demo today.