The Importance of Context in Improving Generative AI

The Importance of Context in Improving Generative AI

Introduction

Artificial intelligence has taken the world to new heights. It is the tool that has revolutionized every industry and resulted in the complete redesign of how companies operate. In the era of AI development services, generative artificial intelligence is the biggest harbinger of change.

Context is essential in being able to provide relevant information. In the most basic form, you have probably seen it while searching.

If you search for the “cat full form” on Google, you might get the Common Admission Test.

However, if you search for “cat in computer science,” you will get computer-assisted Translation Tools.

If you just write “cat” in Google search results, you will be treated to some adorable cat pictures.

The same word does have different meanings depending on the context you are searching. How many times has it happened to you that you were searching for a full form or meaning of a certain term and had to mention the subject to get relevant results?

Generative AI is also responsible for offering you information, generating content, images, videos, or other forms of media based on inputs. If the tool understands the context of the query, relevant answers are possible. Otherwise, you would need to provide additional context and more details to get what you need.

What are Generative AI Development Services?

You have already used a variety of tools created using generative AI development services. The common examples are ChatGPT and Gemini. Of course, there are other applications available online, some for creating text, others for images, and some others for video. Of course, there are multimodal tools that create different types of outputs based on inputs. Additionally, there are tools that can get inputs in more than one way; that is, you can also upload images and videos as inputs and relevant output.

Generative AI development services are what companies leverage to create large language models that that offer relevant information to users.

The Biggest Problem with Generative Artificial Intelligence Success?

All the generative artificial intelligence tools you use leverage foundational AI models like large language models (LLMs). The AI systems have human-level understanding and reasoning abilities. When trying to harness the power of Generative AI, there are several hurdles, including:

  1. Insufficient Business Context

Businesses leverage Gen AI for specific context. As these tools are based on LLMs that are trained on massive data available online, there is a margin for error. This dataset has outdated, static information, and hence finds it challenging to offer industry-specific tasks. Therefore, the responses it produces are both generic and in specific cases, irrelevant, especially when it requires a particular business context.

  1. Limited Skills, Time & Access

Prompt engineering does make it possible for businesses to provide Gen AI models with the right context, however, it includes trial and error in prompts to get the desired result. It is both time-consuming and expensive. Most businesses do not have the specialist skills required to customize these models, provide model governance across different automation and AI teams.

  1. Transparency

Gen AI does not offer reasoning or mention the source data that they are using for their decisions. If you do not know the inner workings of the model, trusting it, especially as a business, is a risky game. This leads to a lack of trust and understanding.

  1. False Positives

Gen AI is prone to mistakes. It can give you detailed answers for things that do not exist. These answers will look convincing; however, a simple Google search might prove them to be incorrect. Hence, the tool needs to be monitored when included in the workflow.

Adding Context to Generative AI

To leverage the products of their preferred AI ML development company, companies must invest in a reliable method to ground their model with their own business data. Training these models in a business context will help businesses get more relevant results while allowing the tool to make fewer mistakes and improve the overall reliability and trustworthiness of the product.

For this, businesses can use retrieval augmented generation (RAG) to provide AI models with relevant context and data. RAG uses the data it has been trained on, along with relevant information from specific datasets like a company’s knowledge base.

Contact Artificial Intelligence Solution Provider for Context Grounding

RAG framework allows you to contextualize Gen AI responses. Companies can use machine learning services to educate the model on their business, industry, and data.

It offers a range of benefits to the companies, including:

  • Transform your generic LLMs into specialist agents

  • Minimize the learning curve

  • Offer more transparency and explainability

  • The end product is more reliable and successful

You will need AI service providers to make this vision come to life.

Understanding Context with AI Development Services

Imagine getting an AI tool that understands the intricacies of language and scenarios, and offers outputs that are indistinguishable from human-generated content. Integration of context using AI development services can help you achieve superior performance and improve the overall user experience and satisfaction.

Now that you understand the importance of context in generative AI, it is time to revolutionize your AI projects. Get in touch with AI services companies and start your journey into the future today.