Why context is the key to better generative AI

Imagine you’ve hired a Michelin star chef to cook for your dinner party. But you give them the assignment without any information about your preferences, dietary restrictions, or the occasion you're celebrating. Now, the chef might whip up something extraordinary. Or everyone might go home hungry.

The same holds true for business. Your company can have the best brains in the world, but they won’t do you much good until they’ve learned the context of your business. And the same goes for generative AI (GenAI).

GenAI models, such as OpenAI’s GPT series or Anthropic’s Claude, represent a powerful new general-purpose technology, capable of powering countless value-driving use cases. However, enterprises won’t achieve GenAI’s full potential until they can help AI understand their unique business context.

Foundational obstacles to GenAI success

GenAI tools are powered by foundational AI models like large language models (LLMs). These complex AI systems have advanced to the point where they have human-level understanding and reasoning abilities. However, like humans, they only know what they know or have been taught to understand.

Businesses, though eager to harness the power of GenAI, face several challenges:

A lack of business context

The LLMs behind GenAI are based on massive datasets from publicly available knowledge bases, like the internet. These are static, often outdated, and usually don’t contain the domain understanding for addressing industry-specific tasks. This results in generic responses that don't meet your objectives. Often, GenAI models can’t answer simple questions that require only a small amount of specific business context.

Limited access, skills, and time

It’s possible to feed GenAI models the right context through methods like prompt engineering. This is the largely trial-and-error process of experimenting with different input prompts to generate the desired response from the model. However, this can be laborious and expensive. Most businesses don’t have the luxury of time. They also lack access to advanced models, and the specialist skills needed to customize them and provide model governance across different automation and AI teams.

A lack of transparency

GenAI models are called ‘black boxes’ for a reason. After all, LLMs are multi-billion-parameter models with intricate semantic relationships that don’t explain their reasoning or the source data that drives their decisions. To put this simply: GenAI doesn’t show its workings, and that’s a problem for regulators and customers. This lack of transparency can misguide decision makers, hindering trust and understanding.

Hallucinations and false positives

Even AI models can make mistakes. GenAI can sometimes ‘hallucinate’, generating very convincing but incorrect answers and insights. If these outputs aren’t reviewed and fact-checked, the consequences can be severe, leading to bad business decisions and ruined customer relationships. As a result, GenAI can’t be ‘left alone’ but must be closely supervised when involved in any workflow.

Retrieval augmented generation: context is king

To maximize the value of GenAI, businesses first need a reliable method for grounding their models in their own business data. Not only will this give models the relevant context, but it’ll also help them act appropriately and make fewer mistakes, improving reliability and trustworthiness.

Retrieval augmented generation (RAG) is a useful method for feeding AI models relevant context and data. RAG doesn't just rely on the data it’s been trained on, but actively seeks out relevant knowledge from a specific dataset (like a company’s knowledge base). 

Imagine you're back in college and you’ve been asked to write an essay. For some topics, you can write based on what you already know. But for more specific questions, you need to look up or 'retrieve' that information from a book or journal. RAG works the same way.

Introducing UiPath context grounding

The RAG framework results in highly precise and contextually accurate GenAI responses. It ‘educates’ your models by giving them a crash course in your business, industry, lingo, and data.

That’s why RAG is a fundamental component of context grounding, the latest addition to the UiPath AI Trust Layer. When a user submits a prompt to a GenAI model, context grounding uses RAG to extract useful information from a relevant dataset. It then uses that information to create responses that are relevant, accurate, and context-sensitive.

As a key part of the UiPath AI Trust Layer, context grounding offers distinct advantages to businesses wanting the best results from GenAI:

Specialized GenAI models

Context grounding helps transform your LLMs from generic to specialized. UiPath has access to multiple UiPath data sources and a flexible framework for both internal and third-party tools to work together. We provide a reliable method for grounding prompts with user-provided, domain-specific data, ensuring that your AI understands and adapts to the unique nuances of your business and industry.

Ease of use and reduced time to value

Context grounding is designed with the user in mind. It provides a simple and intuitive interface that minimizes the learning curve. Businesses can now leverage LLMs that are optimized to create context-specific outputs based on their data.

Enhanced GenAI transparency and explainability

RAG delivers clarity on the data used and the logic behind every GenAI response. The AI decision making process is open for exploration and understanding. In addition, the UiPath AI Trust Layer provides insight and control over your use of generative AI models, and ensures data is treated with the highest levels of governance.

More successful and reliable GenAI

RAG alone will not eliminate hallucinations, but it has been shown to significantly reduce their likelihood. Combined with the UiPath AI Trust Layer, UiPath makes sure GenAI models are delivering reliable, accurately generated responses into automations. We also maintain human in the loop to ensure that context and results are in line with business automation objectives.

When generative AI knows your business

Context grounding makes it easy for businesses to empower GenAI with their own business data, improving performance and predictability. It provides a clear view into the black box, delivering a layer of explainability so GenAI responses can be safely tracked and improved over time. 

Businesses also gain access to more advanced semantic search capabilities. In other words, context grounding can help GenAI understand the 'why' behind a question, focusing on the user’s intent rather than the literal words they use. The result? Less frustration and more accurate and relevant responses. 

How about an example to really put everything in context? A healthcare company wants an efficient method to screen potential organ donors. Normally, clinicians would have to sift through long and complex requirements documents to judge whether a donor was a good fit. But a GenAI assistant, augmented by context grounding, could streamline the entire process.

Instead of searching through the documents, clinicians can just ask the tool whether a donor is suitable. The model would understand the request, retrieve the relevant information, and present it back to the clinician. And just to be safe, it would show the source of this information so its decision can be reviewed.

Foundational models are just that—a foundation. You need to firmly ground GenAI in your business context before you can trust it to take action and drive automation. Also, you need a guiding framework to ensure AI uses data in a governed, traceable, and transparent way. That’s why context grounding is key to GenAI success.

For more information on context grounding, the UiPath AI Trust Layer, and our latest AI innovations, watch our major announcements from the UiPath AI Summit on demand.