LlamaIndex Webinar: RAG Beyond Basic Chatbots

Summary

The video features presentations from the winning teams of a hackathon focused on building advanced AI applications using language models. The projects include an AI assistant to aid home purchases, a crisis counselor co-pilot to automate paperwork, an app to facilitate building accessory dwelling units, and a tool to generate 3D models from natural language prompts using code. The presentations highlight innovative applications of AI, showcasing the capabilities of models like GPT-3 and Rag for tasks beyond basic Q&A while providing inspiration for impactful AI development.

Takeaways

• 😊 The hackathon focused on building advanced AI applications using Rag beyond basic Q&A bots
• 😎 Four winning hackathon projects were featured: Adu Planner, Counselor Co-Pilot, Anything.xyz, and Home AI
• 🏠 Home AI helps simplify the home buying process through natural language search, summarizing disclosures, and offer drafting
• ✈️ Adu Planner analyzes building codes and property layouts to determine where accessory dwelling units can be built
• 🤝 Counselor Co-Pilot assists crisis counselors by providing relevant resources and documentation assistance
• 🔨 Anything.xyz generates 3D models from natural language prompts by outputting build instructions as code
• 📄 The projects used tools like LLama Parse to handle complex documents and LLama Index for retrieval
• 👩‍💻 The teams plan to continue developing the projects, focusing on market validation and refinement
• 💡 Key challenges identified include evaluation approaches for generative AI and integrating human feedback
• 🌟 Presenters were excited by and appreciative of the potential for AI to solve impactful real-world problems

Q & A

What was the theme of the hackathon hosted by Llama Index?

The theme was to build advanced applications using Llama Index and RAG that go beyond basic Q&A bots.

What were the four winning hackathon projects featured in the video?

The four winning projects were: Adu Planner, Counselor Co-Pilot, Anything.xyz, and Home AI.

What was the motivation behind the Adu Planner project?

The motivation was to streamline the process for homeowners to build accessory dwelling units (ADUs) in their backyards in California, helping to address housing shortages.

How did the Counselor Co-Pilot project aim to help crisis counselors?

It aimed to automate administrative tasks like searching for resources and filling out forms so counselors can focus on conversations with people in crisis.

What machine learning frameworks and databases were used by the teams?

They used tools like LLama Index, RAG, LLama Parse, Astra DB, BentoML, and more for capabilities like document parsing, search, and generating suggestions.

How did the Anything.xyz project use code generation for 3D models?

It uses AI to generate Python code that leverages the Build One 2 3D framework to programmatically build 3D models based on natural language prompts.

What was the goal of the Home AI real estate project?

To help home buyers through steps like search, analyzing disclosures, and making offers by summarizing complex information and paperwork.

How did teams create the UI for their hackathon projects?

Teams focused on designing UIs that prioritize user conversations and safety, while surfacing AI-generated suggestions and resources on the side.

What future work did teams propose for their projects?

Ideas included improving performance, incorporating human feedback, finding product-market fit, and exploring multimodal capabilities.

How were some projects able to reduce compute costs substantially?

By leveraging RAG to retrieve only the most relevant examples instead of expanding large prompts.