130+ Projects

Set up Python, virtual environments, and learn basic syntax for data science.

Build a CLI tool to explore REST APIs and understand HTTP requests/responses.

Learn to use Jupyter notebooks for interactive data science and experimentation.

Learn prompt engineering techniques and create effective prompts for different tasks.

Create a command-line application that interacts with ChatGPT or Claude APIs.

Build a library that simplifies interactions with multiple LLM APIs.

Build an interactive chatbot UI using Streamlit with conversation memory.

Create an application that generates code based on natural language descriptions.

Build an app that extracts and summarizes key information from documents.

Build a chatbot that retrieves and references documents in its responses.

Create an autonomous agent that can search the web and synthesize information.

Build a system to analyze and extract patterns across multiple documents.

Build a FastAPI-based API for serving an LLM application at scale.

Create monitoring tools to track performance, costs, and errors in production.

Implement a scalable async job queue for long-running LLM tasks.

Fine-tune an LLM on domain-specific data and deploy for inference.

Build an app using vision models (GPT-4V, Claude Vision) for image understanding.

Create a sophisticated agent that combines vision, text, and action capabilities.

Full-stack application with AI code completion and intelligent suggestions.

Build a collaborative app with WebSockets and AI features supporting multiple users.

Create a mobile app (iOS/Android) powered by GenAI with offline capabilities.

Build and deploy a complete SaaS application with AI features, billing, and analytics.

Engineer a large-scale RAG system supporting millions of documents and high QPS.

Build infrastructure for serving multiple LLMs with caching, batching, and optimization.

Design and implement distributed agents orchestrating complex workflows.

Create tools for benchmarking, profiling, and optimizing AI application performance.

Research and implement innovative RAG approaches with published benchmarks.

Research optimal agent architectures for different problem classes.

Research and publish findings on efficient fine-tuning methods.

Investigate interpretability and explainability in large language models.

Comprehensive user and market research for AI product positioning.

Analyze competitive AI products and create detailed feature benchmarks.

Design comprehensive metrics and analytics frameworks for AI products.

Develop detailed business cases for AI product investments.