GenAI & RAG Project Guidance

Building a GenAI or RAG project but stuck on the architecture, technology choices, retrieval quality, or implementation?

In this 1:1 session, we'll look at your project and work through the technical challenges you're facing.

What we can discuss

• Designing a practical RAG architecture
• Document ingestion and chunking strategies
• Embedding models and vector databases
• Retrieval and reranking approaches
• Query rewriting and retrieval optimization
• LLM selection and prompt design
• Context management and hallucination reduction
• Evaluation of RAG applications
• FastAPI and backend architecture
• Async processing and concurrency
• Production considerations for GenAI applications
• Debugging and improving an existing GenAI project
• Choosing the right tools and technologies

What you'll get

A focused technical discussion around your specific project, with practical recommendations on what to change, improve, or implement next.

This isn't a generic tutorial. Bring your architecture, code, project idea, or specific technical problem and we'll work through it together.

Who this is for

• Developers building RAG applications
• Developers learning GenAI through projects
• Students building AI portfolios
• Software engineers working on LLM applications
• Anyone stuck on a GenAI/RAG implementation or architecture decision

Before the session

Please share your project details, architecture, code, or specific questions beforehand if possible. This helps us spend the session solving the actual problem rather than starting from scratch.