Why context is essential for improving generative AI

Consider that you have a Michelin-starred chef to prepare dinner for your guests. However, you do not tell them about your dietary needs, preferences, or the event you are commemorating when you give them the assignment. The chef could now prepare something truly amazing. Or perhaps everybody goes home hungry.

This also applies to business. Even with the brightest minds on the planet, your organization will not benefit much until they understand the specifics of your industry. Likewise with generative artificial intelligence (GenAI).

GenAI models, like Claude from Anthropic and the GPT series from OpenAI, represent a potent new general-purpose technology that can power a plethora of use cases that add significant value. But until businesses can assist AI in comprehending their particular business context, they will not be able to fully realize the potential of GenAI.

Fundamental barriers to the success of GenAI

Large language models (LLMs), one of the core AI models, enable GenAI technologies. These sophisticated AI systems have developed to the point where they can reason and understand at the same level as humans. They only understand what they have been taught to understand, just like humans do.

Businesses are keen to use GenAI, but they confront a number of obstacles:

Absence of business context

Massively accessible knowledge bases, like as the internet, provide the foundation for the LLMs that underpin GenAI. These are static, frequently out-of-date, and typically lack the subject knowledge needed to do activities unique to a given industry. This leads to general answers that fall short of your goals. Frequently, simple questions requiring only a little bit of particular business context are beyond the scope of GenAI models.

restricted time, abilities, and access

With techniques like prompt engineering, it is possible to provide GenAI models with the appropriate context. This is the process of experimenting with various input prompts in order to get the desired answer from the model, which is mostly done by trial and error. But this can be costly and time-consuming. The majority of enterprises lack the luxury of time. Additionally, they may not have access to sophisticated models or the specialized knowledge required to oversee models across various automation and AI teams and modify them.

An absence of clarity

The term “black box” is applied to GenAI models for a purpose. Ultimately, LLMs are complex multi-billion parameter models with complex semantic links that do not provide an explanation for their decision-making process or the underlying data. To put it plainly, authorities and consumers are concerned because GenAI conceals its inner workings. Decision-makers may be misled by this lack of transparency, which undermines mutual respect and understanding.

Delusions and misleading positive results

AI models are fallible too. Sometimes, GenAI will “hallucinate,” producing answers and insights that are extremely compelling but untrue. Negative company decisions and damaged consumer relationships may result from failing to review and fact-check these outputs. Because of this, GenAI cannot be “left alone” and needs to be closely monitored in all workflows.


Context is king in retrieval-augmented generation.

Businesses must first have a dependable technique for basing their models on their own business data if they are to fully benefit from GenAI. This will improve models’ dependability and trustworthiness by providing them with the pertinent context and assisting them in acting responsibly and making fewer mistakes.

A helpful technique for providing pertinent context and data to AI models is retrieval augmented generation, or RAG. Instead than merely depending on the data it has been trained on, RAG actively searches a particular dataset (such as a company’s knowledge repository) for pertinent knowledge.

Imagine that you are required to write an essay and that you are back at college. You can write about some subjects based on your prior knowledge. However, you must “extract” or look up the material for more focused queries from a book or journal. The same is true for RAG.


Presenting context grounding for UiPath

GenAI replies produced by the RAG framework are extremely accurate and contextually relevant. It provides your models with a crash education in your business, industry, jargon, and data, so “educating” them.

For this reason, RAG is an essential part of the newest feature of the UiPath AI Trust Layer, context grounding. Context grounding employs RAG to extract relevant information from a dataset when a user sends a prompt to a GenAI model. It then makes use of the data to generate accurate, pertinent, and context-sensitive replies.

Context grounding, a crucial component of the UiPath AI Trust Layer, provides clear benefits to companies looking to get the most out of GenAI.

specific GenAI models

Using context grounding makes your LLMs more specific than generic. Multiple UiPath data sources are available to UiPath, and the framework is adaptable enough to allow internal and external tools to collaborate. We offer a dependable way to anchor prompts with user-supplied, domain-specific data so that your AI can comprehend and adjust to the particular quirks of your company and sector.

Simple operation and quicker time to value

The user is considered in the design of context grounding. It has an easy-to-use UI that reduces the learning curve. Now, businesses may use optimized LLMs to generate outputs that are specific to a certain environment using their data.

Improved explainability and clarity of GenAI

RAG provides insight into the data utilized and the reasoning behind each GenAI response. There is room for investigation and comprehension into the AI decision-making process. Furthermore, the UiPath AI Trust Layer guarantees that data is handled with the highest levels of governance and offers you insight and control over how you deploy generative AI models.

While more effective and dependable GenAI RAG has been demonstrated to considerably lower the chance of hallucinations, it cannot completely eradicate them. When used in conjunction with the UiPath AI Trust Layer, UiPath ensures that GenAI models are producing correct and dependable responses for automations. In order to make sure that context and outcomes align with business automation goals, we also keep humans involved in the process.

When generative AI comprehends your industry


Businesses can easily equip GenAI with their own business data by using context grounding, which boosts predictability and performance. It offers a transparent window into the black box, adding an explainability layer to allow GenAI responses to be tracked securely and enhanced over time.

Additionally, businesses get access to more sophisticated semantic search features. To put it another way, by concentrating on the user’s meaning rather than the exact words they use, context grounding can assist GenAI in comprehending the “why” behind an inquiry. What was the outcome? Reduced frustration and increased precision and pertinence of responses.

What if we used an example to truly put things into perspective? An organ procurement organization seeks an effective way to assess possible donors. Typically, in order to determine whether a donor was a suitable match, clinicians would need to go through lengthy and intricate requirements papers. However, a GenAI helper enhanced by context grounding could make the entire procedure more efficient.

Clinicians can query the tool whether a donor is suitable, saving them the trouble of looking through the documentation. After comprehending the request, the model would obtain the pertinent data and return it to the clinician. In addition, it would reveal the information’s original source so that the decision could be double-checked.

Just that—a foundation—is what foundational models are. Before you can rely on GenAI to take initiative and promote automation, you must thoroughly integrate it into your corporate environment. In order to guarantee that AI uses data in a regulated, traceable, and transparent manner, you also need a guiding framework. Context anchoring is therefore essential to the success of GenAI.

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