TruEra Challenge: Build LLM Applications

Let's Kick it Off! We are starting the stream with a Hackathon opening, an introduction to lablab.ai from the NewNative co-founder, and a schedule overview.

Hackathon Kick-Off Stream

Join Anupam Datta, Co-founder, President, and Chief Scientist at TruEra, as he kicks off the Hackathon with insightful opening words, setting the tone for an engaging exploration into TruEra Challenge: Build LLM Applications with Google Vertex AI.

TruEra Opening Words

Let's dive into the platform and its features that will be useful for Hackathon participants.

Hackathon Guide and Platform Walk-through

Join the workshop with Michael Davis, Customer Engineer at Google and Anupam Datta, co-founder, President, and Chief Scientist at TruEra.

Google Cloud x TruEra Workshop: Running LLM-based Evals with PaLM 2

Join us for an insightful workshop featuring Josh Reini, DevRel Engineer at TruEra, and Yujian Tang, Developer Advocate at Zilliz.

Build and Iterate with Advanced Index Construction: Chunking, Range Search and more

General Q&A session on Discord

In this workshop on December 4, Shayak Sen, CTO of Truera, and Laurie Voss, VP of Developer Relations at LlamaIndex, will explore the building and evaluation of multimodal GenAI apps using the RAG Triad model. 

Building and Evaluating Multimodal GenAI Apps: RAG Triad is All you Need

Submissions Deadline

Extended Submission Deadline💻

TOP-5 teams will be invited to pitch their ideas for Hackathon judges - to define the Hackathon winner!

Finalists Stream

Juohmaru

Nithiroj T.

alek

juohmaru

morn

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That is what we aim to do in our project.

We mainly employ Retrieval-Augmented Generation (RAG) to our project. Our technology stack encompasses TruLens as an evaluation tool, LLM and Embedding model from VertexAI, LangChain for crafting diverse chain types, and ChromaDB serving as the vector store.

Our goal is to compare four distinct chain-type techniques: Stuff, Map Rescue, Refine, and Map Re-rank. To assess their effectiveness, we employ the RAG Triad concept and evaluate each chain type across three dimensions: Context Relevance, Groundedness, and Answer Relevance.

For evaluation purposes, we employ approximately 20 questions related to financial analysis. Leveraging TruLens, we observe that the Stuff and Refine chain types score notably high on Groundedness and Answer Relevance, respectively, while the others fall short.

To enhance overall performance scores, several factors, such as varying prompts, need further investigation.


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"Juohmaru team on TruEra Challenge: Build LLM Applications Hackathon"

Team Idea

Submission