What knowledge could probably do for workers, employers, or businesses is that they empower everyone in the business process and help them work at their best.

No work can go underway if knowledge is scarce, outdated, shallow, or incorrect.

This is too often the case with most organizations. They struggle to keep knowledge readily available and accessible via platforms employees use and enable them to work.

There are company websites comprising a wide variety of resources such as how-to guides, content on community forums, articles, release logs, and technical documentation. Internal data resources and resources residing in third-party apps are a go-to knowledge repository.

However, knowledge search is underperforming and time-consuming for tools missing appropriate mechanisms.

Knowledge AI holds enormous potential to allow businesses to expand the automation capability to search, generate, and surface accurate information for employees.

However, no technology can perform in the capacity of a Superman unless it possesses excellent abilities or is open to constantly learning new skills, accepting mistakes, and working on improving those loopholes.

Knowledge AI brings magical power in search and helps knowledge workers find information to perform tasks by relying heavily on large language models or Generative AI.

We can have a sense from this viewpoint that if data is filthy, the search results will become stale and useless.

To bring in so much potential in Knowledge AI, one must ensure that the data it is trained on must be accurate and relevant.

Today, we will learn about Knowledge AI's best practices to accelerate knowledge search and employee productivity.

How about going by this saying: you reap what you sow.

The allegory isn’t to tell the moral of Aesop’s story here – that justifies the good and wrongdoings.

Instead, the good and wrongdoings here are about how you train your architecture model or large language model for Knowledge AI, which decides the results for the knowledge search for your employees.

Businesses have long been concerned about using Generative AI due to the fact that it produces wrong information.

One wrong answer that Generative AI produces for employers can put a bad impression on the business reputation if it is executed. There are a couple of challenges for businesses as they use Knowledge AI in terms of accuracy and relevance.

Why is it that Knowledge AI can surface wrong information?

• Data relevancy

Businesses have constraints on having enough top-line budgets to train large language models. The model ‘as is.’ is preferable and suitable for them to use for business use cases, as they might think.

• It has knowledge resources only till the initial training process.

• Due to the cut-off date of training, it cannot provide insights into the current events.

Companies using industry-wide cases for existing IT or HR issues need help with their processes with unique changing needs.

• Shortage of skilled labor

Companies that want to have custom solutions for more business-specific domain use cases can choose to train their models. But, shallow data and a shortage of skilled or qualified data analysts or specialists could cut short their tryst with a custom Knowledge AI development journey from scratch.

In such a case, a method to oversee model training can become too inconsistent, resulting in lapses in data verification and validation. The need for appropriate monitoring might pass documents with faulty languages or invalid data to be used for model training.

As a result, Knowledge AI can hallucinate and misguide an employee.

• Lack of proper strategy

A nascent technology needs robust monitoring and regulation compliances, raising challenges for stakeholders to set up various mechanisms that can help simplify steps for training processes, monitoring, approval, deployment, and, finally, usage.

In most scenarios, the lack of experience across the technology can take months to strategize priorities and time to market.

“As is,” LLM or Generative models are an easy catch for most business owners as they want to avoid the hassles of the training processes and intricate training process.

The technology is new. However, learning to implement custom training processes can be helpful for you in the long run.

Here is what you can do to build Knowledge AI search functions with outstanding capabilities for your business use cases.

A. Data quality and preprocessing

1. Importance of high-quality data

Data can be repetitive, contain errors, and are not comprehensive for model training. A wrong set of data tends to produce incorrect responses.

AI models learn from continuous learning. If there is a wrong interpretation of figures or sentences, the model observes this and applies it to NLP queries.

For example, training data explains that two ears, four legs, and fur denote a bear, whereas we know it could relate to any four-legged animal. In such a case, we need to be more precise in training our models about these possible scenarios – be it a typical question or an industry-related question.

So, if a model is trained on vague concepts, the result will be misleading. Say a user asks to draw an image of a dog with a similar description – a Knowledge AI model can relate to these entities for a bear only.

Training data having limited context and description can lead to undesirable answers.

This significantly requires data to be comprehensive, grammatically correct, contextual, and meaningful.

We always need to ensure that data, whichever format we want, is clean and error-free. On top of it, data aligns with enterprise objectives.

2. Data preprocessing

You have structured and unstructured data for Knowledge AI training. As we constantly say, one wrong data means misinformation, so preprocessing can help.

It is essentially crucial that you clean data, remove errors, create data in a consistent, standardized format, resize images, remove any duplicate data, and organize it for training.

B. Quality assurance and validation

Preprocessing moves to the next step of data feeding into the model. After you ensure your data is clean and organized, comprehensive scrutiny is essential.

1. Human-in-the-loop validation

Do not leave data to be automated fed into the model. Every step of the data feeding must pass through strict human oversight to ensure that ML algorithms are suitable. If not, human experts must adjust parameters and keep an eye on the shortcomings, if any.

2. Feedback loops for continuous improvement

Constantly monitoring the model's performance is essential to ensure that model behavior is consistent with the desired business objective.

If there is anything wrong, data analysts or other human experts can collect feedback from the iteration team and implement changes for the improvement of model performance.

Helping to retrieve relevant information is as essential as offering accurate information. If information is accurate but irrelevant, it is no longer helpful for an enterprise context.

During the model training, it is another key responsibility of your company and stakeholders to check for data relevancy and help your people work at their best.

A. Context awareness

Domain specificity is essential for industry use cases. Knowledge AI algorithms must have context awareness to improve data relevance and user convenience.

1. Understanding the user's context

In an industry scenario, your Knowledge AI model must understand various business contexts so that users can get answers properly to solve work-related queries or problems.

Say a user wants to get out of an unlocked account. A Knowledge AI-based chatbot must understand that the user is talking about a specific app or application his company uses and help him out.

2. Implementing context-aware algorithms

Say a business wants its people to handle profile updates by themselves. Instead of writing to HR in back-and-forth emails, it can take the help of Knowledge AI to suggest self-help steps in performing essential tasks.

To train the model, its data must contain contextual algorithms that could understand that a user wants it to provide a link that can help him set up his account or fill in personal details in the right system.

B. User feedback integration

1. Importance of user feedback

Just as human expert feedback is essential during the model training and deployment, user feedback is vital to make Knowledge AI feel more intuitive and fast.

User feedback is also essential to know the adoption rate, unlikeliness, challenges, etc.

Making interactions to improve user experience depends on collecting feedback. Make it a priority for Knowledge AI's success.

2. Strategies for collecting and implementing feedback

Periodic feedback is essential to know where Knowledge AI lacks in delivering employee support.

As a business, sending automated feedback forms at a specified interval is ideal to collect user feedback.

C. Knowledge base maintenance

This is a crucial job to maintain knowledge base resources. Based on what comes up as the scenario changes, the provided knowledge in the existing document needs revisions. Without revision, Knowledge AI trained on previous datasets can give answers useless for the current business case.

1. Regular updates and relevance checks

Say your people know ways to work with features of a specific brand of IT product vending machine for a long time now. So, documents Knowledge AI refers to for suggesting help for IT asset assignment may not work if a new vending machine has replaced it.

The functioning of a new vending machine has similarities in many cases but still requires a new learning curve.

Here, Knowledge AI needs new or modified training data for user flexibility.

Knowledge AI solution provider Workativ provides a seamless option within its platform to implement changes and help with change management.

• Content curation and pruning

With each passing day, business use cases will evolve, and Knowledge AI content needs a massive volume of data to learn patterns to surface answers.

It pays off if you have a valuable stack of content with appropriate topics that match your business use cases and employee queries.

The more the data, the more accurate and relevant your search results can become with Knowledge AI.

Sometimes, relevant information is more critical than absolutely accurate messages. The idea is to find precise information pertinent to work.

A. The delicate trade-off

We must constantly evaluate the significance of relevance and accuracy of information for a specific task.

Say a question for ‘how to fill in details for reimbursement claims?’ There could be too many accurate suggestions for this question according to many scenarios. But, if a user asks for steps to raise reimbursement requests for a specific business operation, he expects a relevant answer — not too many accurate answers that otherwise mislead.

Businesses must take this into consideration and prepare Knowledge AI data resources that work better.

B. Strategies for striking the right balance

The effectiveness of striking the right balance is to help avoid any confusion and give your employees the opportunity to work steadily.

Occasional reevaluation of Knowledge AI data works best to remove unnecessary complexity for users.

With that, constant adjustments to the document as business changes according to the user demographics and demands might help in a significant way.

A. Transparency and fairness against potential biases in AI algorithms

A mistake in model training can take a drastic turn as it is susceptible to delivering wrong answers or hallucinating and even being biased.

It is a clear case of discriminatory behavior from an LLM model when it refuses to interact with a specific race, gender, or community.

It can occur if training data has such references.

For example, if you use data from a chat history between a stakeholder and a user that contains contradictory views, your Knowledge AI can learn this and follow this pattern while interacting with a user.

We need to be careful while feeding data into AI models and remove any such references. On the other hand, continuous data evaluation can be helpful to remove potential bias from AI algorithms.

B. Regulatory compliance and ethical guidelines

Like all organizations, we need to be ethically compliant with industry standards and guidelines to eliminate the probabilities of AI unfairness, bias, and misinformation.

We need a proper mechanism for your people to follow the guidelines and be aware of the consequences of unethical use of AI algorithms.

As we strongly need to educate our people on the fair use of AI tools, we also tend to employ an alert to trigger when something unethical incidents happen and take immediate steps to mitigate the risks.

Workativ works with clients to help them automate employee support with Knowledge AI.

Our LLM-powered Knowledge AI platform provides you with the flexibility to build your business use cases and apply them for your employee support.

It is a cost-effective solution for enterprise leaders and even small businesses. Get Knowledge AI today and build a ChatGPT-like question-and-answer chatbot for your employees to help them work at their best.

Are you interested in learning more? Schedule a demo today.