20 Generative AI Projects to Supercharge Your Resume in 2025

Generative AI is reshaping industries, and having hands-on experience with cutting-edge GenAI projects can set you apart in 2025. With AI tools helping employers sift through heaps of resumes, the right project can enhance your resume and showcase your expertise. So, here we bring you 20 projects that will give you a deeper understanding of how GenAI can be leveraged to solve real-world problems. This curated list includes a wide variety of generative AI projects ranging from developing AI assistants and fine-tuning models to building RAG systems and AI agents. We have divided the projects into 3 categories – beginner, intermediate, and advanced – catering to generative AI practitioners of all levels.

Beginner Level Generative AI Projects

Let’s begin by exploring some beginner-level GenAI projects that involve fundamental AI concepts and require basic programming knowledge.

1. Image to Speech GenAI Tool Using GPT-3.5

The project aims to create an AI application that transforms uploaded images into audio short stories. Using OpenAI’s GPT-3.5, LangChain, and some LLMs from Hugging Face, the app can analyze the content of an image, generate a contextual narrative, and then convert it into speech. This functionality provides users with an immersive storytelling experience derived directly from visual inputs.

Problem Statement

Interpreting visual content can be challenging, especially for individuals with visual impairments. Traditional methods of describing images often lack clarity, depth, and personalization. This tool addresses these challenges by automatically generating rich, audio-based narratives from images, enhancing accessibility and offering a novel medium for consumption of visual content.

Key Topics Covered

• Image Analysis: Utilizes computer vision techniques to interpret and extract contextual information from images.
• Generative AI Integration: Employs LLMs from Hugging Face and OpenAI’s GPT-3.5 to craft coherent and contextually relevant stories based on the analyzed image content.
• Speech Synthesis: Converts the generated textual narratives into speech using LLMs.
• Platform Deployment: The project involves deploying the application on Streamlit Cloud and Hugging Face Spaces.

Click here to explore the GitHub Repository.

Note: Although the project uses GPT-3.5, we now have GPT-4 which can build a better version of this voice assistant.

2. GenAI-Powered Career Development Tool

The job market is already streamlined and optimized with AI-powered tools being used for resume filtering and job search. In this project, you will build an AI-driven multi-agent tool designed to support individuals in their career development journey. Leveraging advanced NLP and machine learning techniques, this assistant provides personalized job search assistance and company research. It also does resume analysis and cover letter generation. By integrating multiple AI agents, it offers a comprehensive solution to streamline the job application process.

Problem Statement

Job hunters are often faced with challenges such as crafting tailored resumes and cover letters, identifying suitable job opportunities, and researching potential employers. The GenAI Career Assistant addresses these challenges by automating and personalizing various aspects of the job search process. This multi-agent system employs specific agents for each task, thereby enhancing efficiency and effectiveness for job seekers.

Key Topics Covered

• AI-powered Personalized Job Search: Utilizes AI to match users with job listings that align with their skills and career aspirations.
• Resume Analysis: Employs machine learning algorithms to evaluate and provide feedback on resumes, ensuring they meet industry standards.
• Cover Letter Generation: Automatically crafts customized cover letters based on user input and job descriptions.
• Company Research Summarizer: Gathers and summarizes relevant information about potential employers, aiding in informed decision-making.

Click here to explore the GitHub Repository.

3. Car Buyer Agent Using LangGraph

The Car Buyer Agent is an intelligent system designed to assist users in selecting vehicles that align with their preferences and requirements. Developed using the LangGraph framework, this agent leverages LLMs to process user inputs and provide tailored car recommendations.

Problem Statement

Potential car buyers are often overwhelmed by the vast array of vehicle options available today. It becomes challenging for them to identify models that meet their specific needs. The Car Buyer Agent addresses this issue by offering personalized recommendations, simplifying the decision-making process.

Key Topics Covered

• User Preference Analysis: Utilizes LLMs to interpret and analyze user inputs, ensuring recommendations are aligned with individual preferences.
• LangGraph Framework: Implements the LangGraph framework to structure the agent’s decision-making processes, enhancing efficiency and accuracy.
• Interactive Recommendations: Provides an interactive platform where users can specify their requirements and receive real-time, customized vehicle suggestions.

Click here to explore the GitHub Repository.

Note: You can use CrewAI, AutoGen, or any other agent-building tool instead of LangGraph for this project.

4. Personal Voice Assistant Using GPT-3.5 and Whisper

In this project, you will build a personal voice assistant using Python. This voice assistant leverages OpenAI’s GPT-3.5 for natural language understanding and response generation. It also uses the Whisper model for audio transcription. The AI assistant first captures user voice commands and transcribes them into text. It then processes the input to generate appropriate responses, and delivers these responses audibly as a voice output.

Problem Statement

Voice-activated interfaces such as home assistants, mobile assistants, etc. have become increasingly prevalent these days. This has led to a growing need for accessible and efficient voice assistants that can understand and interact with users using natural language. This project guides you to build a minimalistic yet functional voice assistant that facilitates seamless human-computer interaction through speech.

Key Topics Covered

• Voice Recognition: Captures and transcribes user voice commands using the SoundDevice library.
• Conversational AI: Uses OpenAI’s GPT-3.5 model to interpret user input and generate contextually relevant responses.
• Text-to-Speech Conversion: Uses the pyttsx3 library to convert text responses into speech, enabling auditory interaction.

Click here to explore the GitHub Repository.

Note: Although the project uses GPT-3.5, we now have GPT-4 which can build a better version of this voice assistant.

5. Data Science AI Assistant with Gemma 2b-it

This project leverages Google’s Gemma 2b-it model to build an AI tool that assists users in executing data science tasks. By integrating this advanced language model, the AI assistant can explain complex data science concepts and provide relevant Python code examples. Its aim is to enhance the user’s ability to tackle various data-related challenges.

Problem Statement

The complexities of data science can often be daunting to handle, especially for those new to the field. The vast array of concepts, techniques, and coding practices often presents a steep learning curve. The Data Science AI Assistant addresses these challenges by bridging the gap between theoretical knowledge and practical application. It offers clear explanations and practical coding examples, helping data scientists work easier and faster.

Key Topics Covered

• AI-powered Concept Explanation: Utilizes the Gemma 2b-it model to provide detailed and comprehensible explanations of various data science concepts.
• AI as a Coding Tool: Generates Python code snippets that correspond to the explained concepts, facilitating hands-on application and learning.

View the Kaggle Notebook here.

Now lets get to some slightly difficult, intermediate-level GenAI projects that integrate multiple AI models and may require working with APIs. These projects involve a mix of NLP, retrieval, and automation.

6. Video Analyzer Using Llama3.2 Vision and OpenAI’s Whisper

A video analyzer is a comprehensive tool that generates detailed descriptions of video content. It provides users with a deeper understanding of video materials by extracting key frames and transcribing audio. The tool works by integrating computer vision, audio transcription, and natural language processing. In this project you will be building a video analyzer using vision models like Llama3.2 Vision and OpenAI’s Whisper.

Problem Statement

In the digital age, vast amounts of video content are generated daily, making it challenging to efficiently analyze and comprehend this information. Traditional methods of video analysis are often time-consuming and require significant manual effort. A video analyzer addresses this issue by automating the extraction of key visual and audio elements to offer concise and accurate descriptions of visual content.

Key Topics Covered

• Computer Vision: Utilizes OpenCV for video processing and key frame extraction.
• Audio Processing: Employs OpenAI’s Whisper model to transcribe audio content accurately.
• Natural Language Processing: Incorporates Llama’s 11B vision model to analyze visual data and generate coherent descriptions.

Click here to explore the GitHub Repository.

7. Serverless Video Summarization Using AWS

This project demonstrates an automated solution for creating comprehensive summaries of video content. The video summarizer tool leverages Amazon Bedrock with the AI21 Labs Jurassic-2 Ultra model, to be serverless. The workflow involves extracting images from each frame of the video presentation and generating corresponding text summaries. These are then consolidated into a PDF report, combining each frame’s image with its respective text summary.

Problem Statement

Efficiently summarizing and understanding videos has become increasingly challenging owing to the amount of video content being generated lately. Traditional methods of video summarization are mostly manual, time-consuming, and often impractical at scale. This project addresses these challenges by automating the extraction of key visual elements and generating concise textual summaries. Being serverless, makes it a cost-efficient, fast, and scalable solution.

Key Topics Covered

• Serverless Architecture: Utilizes AWS services to build a scalable and cost-effective serverless solution for video processing and summarization.
• Generative AI Integration: Employs Amazon Bedrock with the AI21 Labs Jurassic-2 Ultra model to generate accurate and contextually relevant text summaries for each video frame.
• Automated Reporting: Generates PDF reports that merge each frame’s image with its corresponding text summary, providing a comprehensive overview of the video content.

Click here to explore the GitHub Repository.

8. LLM-based Finance Agent

The LLM-based Finance Agent is an intelligent system that leverages LLMs to automate financial news retrieval and predict stock prices. It fetches relevant financial news and utilizes historical stock data to forecast future price movements. The agent integrates natural language processing (NLP) and machine learning techniques to provide up-to-date information and financial analysis.

Problem Statement

Staying updated with relevant news and accurately predicting stock price movements are critical yet challenging tasks in the financial sector. Traditional methods often involve manual data collection and analysis, which can be time-consuming and prone to errors. The LLM-based Finance Agent addresses these challenges by automating the retrieval of latest financial news and employing advanced models to predict stock prices.

Key Topics Covered

• Automated News Retrieval: Utilizes LLMs to automatically fetch and process financial news articles.
• Stock Price Prediction: Employs machine learning algorithms to analyze historical stock data and forecast future price trends.
• Natural Language Processing: Applies NLP techniques to interpret and summarize financial news.

Click here to explore the GitHub Repository.

9. Azure Text-to-Speech Model with Avatar

The ‘Azure Talking Avatar’ project integrates Microsoft’s Azure Text-to-Speech (TTS) service with avatar animation. This enables the conversion of text into spoken words accompanied by a visual representation of a talking avatar. The application allows users to input text, select from various avatar styles and languages, and generate videos where the chosen avatar speaks the provided text.

Problem Statement

Creating engaging and interactive content often requires synchronizing speech with visual representations, which can be time-consuming and technically challenging. This project provides an automated solution that combines TTS with avatar animations. It aims to simplify the process of producing dynamic and accessible multimedia content.

Key Topics Covered

• Text-to-Speech Integration: Utilizes Azure’s TTS service to convert written text into natural-sounding speech.
• AI-powered Avatar Animation: Synchronizes speech output with AI generated animated avatars.

Click here to view the GitHub Repository.

10. Adaptive Learning Agent Using LangGraph

In this project, you will build an advanced learning agent that integrates the Feynman technique with LangGraph. The Feynman technique involves explaining complex concepts in very simple terms, as if teaching a child. LangGraph, a framework for building agentic and multi-agent applications, provides the structural foundation for the agent’s operations. The agent guides learners through a sequence of defined but customizable checkpoints, verifying understanding at each step and providing Feynman-style teaching when needed.

Problem Statement

Understanding intricate subjects often poses challenges, especially when learners come across complex concepts without effective ways to simplify them. The Adaptive Learning Agent addresses this issue by employing the Feynman technique within an AI agent framework. This enables users to break down complex topics and understand them more efficiently.

Key Topics Covered

• LangGraph Framework: Utilizes LangGraph to orchestrate the agent’s workflows, providing precision and control in agentic applications.

Click here to checkout the GitHub Repository.

Note: You can use CrewAI, AutoGen, or any other agent-building tool instead of LangGraph for this project.

11. AI-Powered Sales Call Analyzer Using LangChain

This project requires you to build an intelligent system capable of analyzing sales call recordings to extract valuable insights. The sales call analyzer tool leverages frameworks like LangChain and CrewAI to transcribe audio, assess sentiments, and identify the key topics discussed in the call. It can also evaluate the effectiveness of sales strategies employed during the calls.

Problem Statement

Sales teams often face challenges in evaluating and improving their communication strategies due to the manual and time-consuming nature of reviewing call recordings. This project addresses these challenges by providing an automated solution that analyzes sales calls, offering insights into customer interactions and sales techniques, thereby facilitating data-driven improvements in sales performance.

Key Topics Covered

• Audio Transcription: Converts sales call recordings into text format for further analysis.
• Topic Modeling: Identifies and categorizes the main subjects discussed during the calls.
• Sentiment Analysis: Evaluates the emotional tone of the conversations to gauge customer satisfaction and engagement.
• Sales Strategy Evaluation: Assesses the effectiveness of sales techniques used, providing feedback for improvement.

Click here to explore the GitHub Repository.

12. AI Music Composer Using LangGraph

In this project, you will develop an AI-powered music composition system using LangGraph, a framework designed for creating workflows with language models. You will build an agent capable of generating original musical pieces by leveraging advanced language models and structured workflows. It will have the ability to generate tunes, background music, sound effects, and more, just like a human music composer.

Problem Statement

Composing music traditionally requires extensive knowledge of music theory along with creativity. This sometimes poses a challenge to creative artists without formal training. This project gives everyone the chance to compose their own music and bring out their creative side, even without much technical knowledge. The AI agent automates the process of music composition, making it easier for anybody to try a hand at it.

Key Topics Covered

• AI-Driven Music Composition: Demonstrates how to utilize language models to generate musical compositions.
• LangGraph Framework: Illustrates the application of LangGraph in structuring workflows for complex tasks, such as music composition.

Click here to explore the GitHub Repository.

Note: You can use CrewAI, AutoGen, or any other agent-building tool instead of LangGraph for this project.

13. AI-Powered Legal Document Analyzer

This project builds an AI-driven tool to assist legal professionals in analyzing and interpreting complex legal documents. By leveraging advanced NLP techniques, the agent can identify, extract, and summarize key clauses within lengthy contracts and agreements. This streamlines the document review process.

Problem Statement

Reviewing extensive legal documents is often a time-consuming and meticulous task for legal practitioners. Manually sifting through numerous clauses to find pertinent information can lead to inefficiencies and potential oversights. This project addresses these challenges by automating the extraction and summarization of critical clauses. It thereby aims to enhance the accuracy and efficiency of legal document analysis.

Key Topics Covered

• Natural Language Processing: Employs NLP techniques to comprehend and process legal language.
• Clause Extraction: Automatically identifies and extracts significant clauses from legal documents.
• Summarization: Provides concise summaries of extracted clauses and essential terms and conditions.
• Legal Document Analysis: Assists in the thorough examination of contracts and agreements, ensuring critical elements are not overlooked.

Click here to checkout the GitHub Repository.

14. Project Manager Assistant Agent

The Project Manager Assistant Agent is an AI-driven tool designed to assist project managers in organizing and managing tasks effectively. Leveraging advanced NLP capabilities, this agent can interpret project descriptions and generate actionable tasks. It demonstrates how generative AI can help streamline the project planning process.

Problem Statement

Project managers often face challenges in breaking down complex project descriptions into manageable tasks, which can lead to inefficiencies and oversight. This agent addresses these challenges by automating the task generation process. It ensures that all aspects of a project are accounted for and organized systematically.

Key Topics Covered

• Natural Language Processing: Utilizes NLP techniques to comprehend and process project descriptions.
• AI-powered Task Generation: Automatically creates actionable tasks from project descriptions.
• Project Management Integration: Integrates with existing systems and organizes tasks within project management frameworks.

Click here to explore the GitHub Repository.

15. RAG Using Llama3, LangChain, and ChromaDB

This project demonstrates the creation of a Retrieval Augmented Generation (RAG) system by integrating Llama3, LangChain, and ChromaDB. The RAG system enables users to query their documents, even if the information wasn’t included in the training data of the LLM. It achieves this by performing a retrieval step to fetch relevant documents from a vector database where these documents have been indexed.

Problem Statement

Traditional LLMs may not have access to specific, up-to-date, or proprietary information contained within user documents, limiting their ability to provide accurate responses to certain queries. This project addresses this limitation by implementing a RAG system that combines retrieval-based and generation-based models, allowing the LLM to access and utilize external documents during the response generation process.

Key Topics Covered

• Llama3: Utilizes Meta’s Llama3 to generate human-like text based on input queries.
• LangChain: Employs LangChain to streamline the creation of applications that integrate LLMs with other computational resources or knowledge bases.
• ChromaDB: Implements ChromaDB to enable efficient retrieval of relevant documents based on similarity to the input query.

Click here to explore the GitHub Repository.

Advanced Level Generative AI Projects

Here are some advanced projects for the more experienced AI developers and GenAI practitioners. These projects involve fine-tuning LLMs, deploying RAG, optimizing inference, or integrating complex multi-agent workflows.

16. AutoDev: Software Development Agent System

AutoDev is an innovative framework designed to automate software development tasks using AI-driven agents. It enables users to define complex software engineering objectives, which are then executed by autonomous AI agents. These agents are capable of performing diverse operations on a codebase, including file editing, retrieval, building, testing, execution, and version control operations. The framework integrates seamlessly with JetBrains IDEs, such as IntelliJ IDEA and PyCharm, through a dedicated plugin, enhancing the development experience by providing AI-assisted coding functions.

Problem Statement

The increasing complexity of software development requires tools that can automate repetitive and intricate tasks, in order to reduce manual effort and possible errors. Existing AI-powered coding assistants often have limited capabilities, primarily focusing on suggesting code snippets without the ability to perform comprehensive development tasks. AutoDev addresses this gap by offering a fully automated AI-driven development framework that autonomously plans and executes intricate software engineering tasks.

Key Topics Covered

• AI Agents for Software Development: Deploys autonomous AI agents capable of executing various operations on a codebase. This includes file editing, code retrieval, building, testing, execution, and version control.
• IDE Integration: Provides a plugin for JetBrains IDEs, such as IntelliJ IDEA and PyCharm.

Click here to explore the GitHub Repository.

17. Medical RAG Using BioMistral 7B

This project involves the development of a Medical Retrieval-Augmented Generation (RAG) application using an open-source stack. It integrates BioMistral 7B, a language model tailored for medical applications, with PubMedBert for embeddings. It uses Qdrant as a self-hosted vector database and orchestrates workflows using LangChain and Llama.cpp.

Problem Statement

Accessing and synthesizing relevant medical information from vast datasets is challenging. This project offers a solution to this by combining specialized language models with efficient retrieval systems. The resulting RAG system aims to enhance information accessibility in the medical field.

Key Topics Covered

• BioMistral 7B Integration: Utilizes a medical-specific language model to enhance the quality of generated content.
• PubMedBert Embeddings: Employs PubMedBert to generate precise embeddings for medical texts.
• Qdrant Vector Database: Implements Qdrant for efficient vector storage and retrieval.
• LangChain and Llama.cpp Orchestration: Coordinating various components using LangChain and Llama.cpp frameworks.

Click here to explore the GitHub Repository.

18. AI-Powered End-to-End Unit Testing Agent

The AI-Powered Unit Testing Agent is an intelligent system designed to automate the process of end-to-end testing in software applications. Leveraging advanced AI techniques, this agent is capable of generating test scenarios, executing tests, and analyzing outcomes to ensure the robustness and reliability of software systems.

Problem Statement

Manual end-to-end testing is often labor-intensive, time-consuming, and prone to human error. This makes it challenging to maintain comprehensive test coverage as software systems evolve. The AI-Powered Unit Testing Agent addresses these challenges by automating the testing process, thereby enhancing efficiency, accuracy, and scalability in software quality assurance practices.

Key Topics Covered

• Automated Test Generation: Utilizes AI to create diverse and comprehensive test scenarios that mimic real-world user interactions.
• Agentic Test Execution: Implements mechanisms to automatically run generated tests across various environments and configurations.
• Outcome Analysis: Employs AI-driven analysis to interpret test results, identify failures, and suggest potential fixes.
• Continuous Integration Compatibility: Integrates seamlessly with CI/CD pipelines to ensure continuous testing and rapid feedback during the development lifecycle.

Click here to explore the GitHub Repository.

19. On-device RAG Project Using ObjectBox and LangChain

In this project you will develop an on-device RAG application from end-to-end, using ObjectBox’s Vector Database and LangChain. The project guide shows you how to augment a language model’s knowledge base actively, ensuring AI can access and reason with data without it ever needing to leave the device.

Problem Statement

Enhancing language models with up-to-date, context-specific information while maintaining data privacy and security is challenging. This project addresses these challenges by integrating on-device vector databases and retrieval-augmented generation techniques.

Key Topics Covered

• On-Device AI: Implements AI applications that process and store data locally to enhance privacy and reduce latency.
• ObjectBox Vector Database: Uses ObjectBox’s vector database for efficient on-device data storage and retrieval.
• LangChain Integration: Employs LangChain to manage and streamline interactions between the language model and the vector database.

Click here to explore the GitHub Repository.

20. Fine-Tuning Llama 3 with PyTorch FSDP and QLoRA

This project demonstrates efficient fine-tuning of the Llama 3 model using PyTorch’s Fully Sharded Data Parallel (FSDP) and Quantized Low-Rank Adaptation (QLoRA) techniques. The approach leverages Hugging Face’s libraries—Transformers, PEFT, and Datasets—to optimize the fine-tuning process.

Problem Statement

Fine-tuning large language models like Llama 3 can be resource-intensive and time-consuming. This project addresses these challenges by implementing FSDP and QLoRA, which aim to reduce memory consumption and computational overhead during the fine-tuning process.

Key Topics Covered

1. PyTorch FSDP: Utilizes PyTorch’s FSDP to shard model parameters across multiple GPUs, enhancing memory efficiency.
2. QLoRA: Implements QLoRA for parameter-efficient fine-tuning, reducing the number of trainable parameters without significant performance loss.
3. Hugging Face Integration: Incorporates Hugging Face’s Transformers, PEFT, and Datasets libraries to streamline model training and data handling.

Click here to explore the GitHub Repository.

Conclusion

Building generative AI projects is not just about coding – it’s about solving real-world challenges, innovating with GenAI, and expanding your skill set. Whether you start with a personal voice assistant or dive into fine-tuning LLMs, each project on this list will help you gain valuable experience and strengthen your portfolio. As AI continues to evolve, staying ahead of the curve with hands-on projects will give you a competitive edge in the job market. So, pick a project, start building, and let your AI journey take off in 2025!

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Sabreena is a GenAI enthusiast and tech editor who’s passionate about documenting the latest advancements that shape the world. She’s currently exploring the world of AI and Data Science as the Manager of Content & Growth at Analytics Vidhya.