Hi everyone, my name is, so this session of Con 42 Cy Engineering event of 2025 will focus on contextual understanding and ation with high dimension using generative artificial intelligence. The session, focus on the solution approach. So that enhances contextual understanding and ation by first encoding the user inputs into the high dimensional embeds. And these embeddings are then used for the efficient sematic search and of the relevant context, which is inter integrated into prompt. So for the chat completion model that combines the powerful sematic similarity capabilities with advanced language integration, enabling accurate and context of our interactions. So with so much of information, data, abundance documents, review and analysis is a difficult task. The challenge is with huge volume and unstructured data's tough to extract meaningful information from the data and respond in a very personalized and domain or contextual manner. So business and product outcomes will be impacted to not able to derive insights on those specific domain or context due to huge amount of data growth. So there is no solution for domain specific knowledge for ai applications. There is no efficient solution for context Adaptability. So sentiment analysis model or natural language processing or other models. So have a lesser accuracy on the context analysis. This is all the solution architecture. So the approach allows to do provide domain information available with the uploaded. In the chat model what we develop. So the uploaded document will be converted to text and text embedding is done a vector opting through text embedding model, or stored in the vector dp. For any new prompt or query ask, it would do a similarity search from the list of similar text and as and is filter on the. Domain based response is return for the query of the prompts. And fine tuning helps to produce the best results by pertaining with large amounts of information upfront and domain specific knowledge based on the document analysis is done. Analyzing the sentiment domain. Context and the content of the upload document, it generates responses that are not only relevant, but also empathetic on the circumstances. Threshold helps to build the accurate context with the prompt sent. So with this, we would get the right context to provide a final response considering the domain in the text and the text ratings to capture patterns and semantics in, data is obtained by converting the uploaded document into text using certain passes. So for our document analysis and yeah, thereby with this approach, it allows to provide a domain information available in the uploaded document. So this is how the solution we have this. Web where we can see that can, there's option to upload the document, what you wanna derive the knowledge based on the domain or the context. So here I've uploaded English or science textbook and that the uploaded document content is seen as shown here. And yeah, like in the chat model, if the user ask anything related to the domain or the document we'll get a better accuracy. The AI assistant would provide the response to the prompt, to the query based on the, OR domain ready inside the, on the.