Cloud Artificial Intelligence (AI) providers and the huge potential they have for Enterprises

This year has been a real turning point in business applications for solving problems that traditional programming languages (.NET, Python, Java etc.) have long struggled with. Software Engineers are blending their applications with new accessible cloud Artificial Intelligence (AI) systems to deliver exceptional performance in solving challenges.

In this article, I would like to show how to build rules and regulation validation system using traditional programming along with artificial intelligence (AI). Our objective is to fetch relevant content from the variety of sources, unstructured data (pdf, image, docs) structured data (table data) using AI and process this content and make a thoughtful answer.

Let’s divide the entire process into two-part Data Ingestion and Data Retrieval.

Data Ingestion process:

Data ingestion is the process of importing data from various sources into a centralized location such as Azure Cognitive Search. It has Vector search capabilities for indexing, storing, and retrieving vector embeddings from a search index. It also supports features like similarity search, multi-modal search etc. to find the relevant content.

During Data ingestion, we might need to split the large data into chunks and vectorize these chunks. Those Vectors along with data chunks reference form a powerful Knowledge Database that can be queried through Azure Cognitive Search.

Data Retrieval Process:

When user ask a question, it is passed into the Planner Model. Planner will decide which plan need to follow based on the question pattens. Planner performs the basic steps to generate the response. For example:

• Appropriate question preparation based on the past conversation and question.
• Convert the question into vector representation form and send it to Azure AI Search to find relevant information.
• Search results passed to the LLM. Popular libraries like LangChain or Semantic Kernel help chaining the model and pass the response from one model to another.
• Finally, it is prepared the response as user expecting or else system find best way to visualize the data from the predefined set (like Scatter Plot, Line Graph, Pie Chart).

Enhancement/Scaling:

Cloud infrastructure will help to automate the process in the background. For example, Azure Service Bus can help to queue the requests and start processing immediately or as scheduled. We can setup various processing unit for specific task.

Conclusion:

AI enthusiasts have had an amazing year of progression since the launch of GPT-3. Now we see Organizations catching up and realizing power of AI. They are rushing to adopt this cutting-edge technology and place it into their business or workspace. Software Engineers looking to start their own learning journey should be excited for the opportunities ahead. AI still evolving and maturing, it’s opening many aspects and opportunities and impacting our business and society.

Best practices to implementing AI or developing software with it have yet to form, but the strategic value to harnessing AI capabilities is clearly emerging as a pivotal determinant for organizational success.