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RAG Hackathon Questions

Use case 1: Virtual recruiter​

The virtual recruiter, powered by generative Al, promises to revolutionize the recruitment process by automating tasks and personalizing interactions for both candidates and recruiters. Here's how it could work:

Key features​

Automated Resume Matching and Ranking:

  • SemanticMatching: Beyond word matching, the Al identifies semantic links between the candidate's profile and the job requirements, considering transferable skills and relevant experiences.

Personalized Interview Question Generation:

  • Adaptive Questionnaires: Leveraging information from resumes and job descriptions, the Al generates tailored interview questions specific to each candidate.

Overall, a virtual recruiter powered by generative Al can make the recruitment process more efficient, personalized, and equitable. By automating routine tasks, providing data-driven linsights, and tailoring interactions, it can benefit both candidates and recruiters, ultimately leading to better hiring outcomes.

Data : Sample resumes

Use case: 2: Intelligent Insurance Claims Processing with Large Language Models​

Build an Al-powered insurance claims processing application that leverages large language models (LLMs) to automate key tasks, improve accuracy, and expedite claim resolution. The lapplication should:

  1. Intelligent Data Extraction
    1. Use LLMs to intelligently extract relevant information from claims forms (PDFs, images, text)
    2. Handle variations in form formats, languages, and terminology
  2. Policy Verification
    1. Ingest and comprehend insurance policy documents
    2. Cross-reference extracted claim details against policy terms and coverage
  3. Initial Claim Decision
    1. Leverage LLM's natural language understanding to analyze claim validity
    2. Generate initial claim decisions (approve, deny, request more info) with rationale

Data : Historical claims data, insurance policies, and claims forms.

Use case: 3: Financial Analysis and Trading Chatbot​

To develop a chatbot that can ingest and analyze large amounts of financial data in multiple formats (e.g., PDF, documents, images, videos) using a multi-modal model, and provide detailed insights into |market analysis, trends, company performance, and investment decisions.

  1. Multimodal Data Ingestion
    1. Use state-of-the-art multimodal models to process text, images, PDFs, videos, and other data formats
    2. Create vector embeddings for efficient retrieval and analysis
  2. Information Retrieval and Extraction
    1. " Leverage retrieval-augmented generation(RAG) models to scan through large volumes of data
    2. Extract relevant information, insights, and sentiment related to companies, industries, and markets
  3. Natural Language Interface
    1. Provide a conversational interface for users to ask questions and receive detailed analysis
    2. Support queries on market trends, company performance, investment opportunities, and more

Data : Financial data in multiple formats, including news articles, market reports, and social media data.

Use case: 4: Intelligent Product Image Generation and Manipulation​

Develop an Al-powered application that can understand product images, generate multiple relevant backgrounds, and manipulate the images based on customer prompts or requirements. The application should:

  1. Product Image Understanding
    1. Use computer vision and multimodal models to analyze and comprehend the product in a given image
    2. Identify the product category, features, and relevant contextual information
  2. Background Generation/Replacement
    1. Leverage generative Al models to create multiple background variations for the product image
    2. Ensure the generated backgrounds are contextually relevant and visually appealing
    3. For product images with existing backgrounds, provide the ability to replace the background with a new, relevant one
  3. Customer Prompt-based Image Manipulation
    1. Allow customers to provide natural language prompts or instructions for modifying the product image
    2. Use multimodal mode!s to understand the prompts and make the requested changes (e.g.,change product color, add accessories, adjust lighting, etc.)

Data: Product images with and without backgrounds, and customer prompts for image updates.

Scenario: Imagine a large corporation with mountains of legal documents, contracts, policies, and historical case information. Navigating this vast internal document dump can be time consuming and frustrating for legal professionals seeking answers to specific questions.

Enter the Legal Assistant application. This Al-powered tool empowers legal staff by transforming the document pile into an easily searchable and insightful knowledge base.

Key features:

  • Data ingestion and processing
  • Question and answering
  • While giving answers Sensitive information tagging
  • Contextualized results

Overall, the Legal Assistant application empowers legal professionals by transforming internal documents into a valuable knowledge resource, streamlining workflows, and ensuring sensitive information remains protected.

Use case 6: GenAI personalized recommender​

Scenario: Sarah wants to buy a birthday gift for her husband, David, who loves sports. Instead of browsing through endless product categories, she uses a GenAl-powered product search feature.

Here's how GenAI personalizes the search:

  • Contextual understanding: Based on the search intent, identify the right set of products
  • Personalised recommendations: Based on the available product catalogue, user intent, make the list of recommendations
  • Have interactive session to further refine the list of recommended products.

Use case: 7: Intelligent Review Summarization for E-commerce Products​

Develop an Al-powered application that can intelligently summarize online product reviews, |providing users with a concise and insightful overview. The application should:

  1. Review Data Ingestion
    1. Collect and ingest product reviews from various e-commerce platforms (Amazon, Walmart, Best Buy, etc.)
    2. Handle reviews in different languages and formats (text, images, videos)
  2. Sentiment Analysis
    1. Use natural language processing (NLP) and multimodal models to analyze the sentiment of each review (positive, negative, neutral)
    2. ldentify and categorize reviews based on sentiment scores
  3. Key Aspect Extraction
    1. Extract and categorize key aspects or topics discussed in the reviews (e.g., product quality, features, customer service, value for money)
    2. Identify the most frequently mentioned aspects and their associated sentiments

Data: Sample online reviews like one below.

Customers say - https://www.amazon.in/Duracell-Slimmest-Charging-Portable-Simultaneously/dp/B0BJV4L36G/ref=sr_1_1_sspa?crid=AGVPNZMYB98H&dib=eyJ2IjoiMSJ9.afXD21uOAWJpu5Vn7hGg9AMThE6sYsI8X_sV-ZHXm0g.GdugyvGsCPBTi9rx7eV4Q4NymOs27X9onUWQzLgf7yg&dib_tag=se&keywords=Duracell-Slimmest-Charging-Portable-Simultaneously&qid=1723463554&sprefix=duracell-slimmest-charging-portable-simultaneously%2Caps%2C206&sr=8-1-spons&sp_csd=d2lkZ2V0TmFtZT1zcF9hdGY&th=1

GenAI Review Summarization Example

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