Jerry Liu
where else to find them
linkedin:jerry-liu-64390071
what they talk about
where they keep going back
no series more than once — all 6 in the filters
on a stage with
never shared a talk
who talks about the same things
- Amir Siddiqui7 talks
- Max Gfeller4 talks
- Pattaniyil2 talks
- Dan Gibson3 talks
- Sophie Soliven2 talks
- Jeff Vestal5 talks
| title | conference | date |
|---|---|---|
| Building AI Agents that actually automate Knowledge Work | AI Engineer World's Fair 2025 | 2025-06-03 |
A practical blueprint for automating document-heavy workflows: a document MCP server composed of connectors, parser/extractors, and vector/SQL/graph indexes… | ||
| Building Multi-Agent RAG Systems | SingleStore Now 2024 | 2024-10-03 |
Argues that production-grade knowledge assistants need four concrete layers: high-quality parsing and retrieval (not naive 512-token chunking), agentic… | ||
| Building an Advanced Knowledge Assistant | SingleStore Now 2024 | 2024-10-03 |
Explains a concrete stack: use a better parser (LlamaParse) to convert PDFs PowerPoints Excel into semantically coherent chunks, store text and images for… | ||
| The Future of AI Assistants | Ray Summit 2024 | 2024-09-30 |
Argues that naive RAG fails for complex enterprise data and shows concrete fixes: an LLM-native parser (Lama Parse) that extracts tables, charts and captions… | ||
| Keynote: Unlocking Corporate Data with Retrieval-Augmented Generation | AI.dev: Open Source GenAI & ML Summit North America 2023 | 2023-12-12 |
A practical roadmap for moving beyond basic RAG: use LlamaIndex to connect drives, PDFs, databases and APIs to an LLM (quickstart in five lines of code) but… | ||
| Keynote: Unlocking Corporate Data with Retrieval-Augmented Generation | Cassandra Summit 2023 | 2023-12-12 |
Retrieval-augmented generation handles simple fact lookup but fails on multi‑step comparison, SQL‑style filtering, and structured analytics; solution is an LLM… | ||
| Keynote: Building and Productionizing RAG | PyData Global 2023 | 2023-12-06 |
Concrete recipe: a Python library called llama index ingests documents by chunking and embedding into a vector DB, then runs retrieval plus LM synthesis —… | ||
| nothing matches | ||