@jerryjliu0: We're not Palantir, but we do think a lot about evals and hillclimbing w.r.t. document processing. If you have really h…
Summary
Jerry Liu promotes LlamaParse and LlamaAgents for large-scale document extraction, emphasizing LLM evals and hillclimbing for accuracy and cost. He also connects FDE work with evals and RL environments.
View Cached Full Text
Cached at: 08/10/26, 01:37 PM
We’re not Palantir, but we do think a lot about evals and hillclimbing w.r.t. document processing.
If you have really hairy problems around large-scale extraction over complex, real-world document corpuses that require specific constraints around accuracy and/or cost, come talk to us! We’ll work closely with your team to make sure that it’s well optimized.
https://llamaindex.ai/contact
Talk to us
Source: https://www.llamaindex.ai/contact
Let’s build something amazing together
Join leading organizations that automate document ingestion with LlamaParse, the agentic document processing platform. Schedule a tailored demo with our team to discover how LlamaParse can transform your document workflows.
-
Industry-leading accuracyfor document OCR, information extraction, and indexing. -
Trusted outputswith page-level citations, confidence scores, and bounding boxes for human-in-the-loop review. -
Balance cost and accuracyby auto-selecting the best parsing settings with our cost optimizer mode. -
Power end-to-end automation with LlamaAgentsour low-code builder for developing and deploying document agents from natural language. -
-
-
-
-
Compliance
Open Source
Jerry Liu (@jerryjliu0): The future of FDE work seems closely related with all work around evals/posttraining/RL envs.
FDEs are effectively responsible for the following:
- Define the business problem.
- Codify the business problem into an eval rubric and environment.
- Hillclimb the environment
Similar Articles
@jerryjliu0: Fully solving document parsing includes covering every point on the Pareto curve of accuracy, cost, and latency: High-a…
Jerry Liu presents a framework for document parsing across accuracy, cost, and latency tradeoffs, introducing LiteParse as an open-source, low-latency parsing tool for AI agent loops, along with LlamaParse for high-accuracy modes.
@jerryjliu0: Our core mission today is using AI to solve document OCR. All of our product offerings, from commercial (LlamaParse) to…
LlamaIndex has revamped its website and reaffirmed its core mission of AI-powered document OCR, with offerings including commercial product LlamaParse and open-source tools LiteParse and ParseBench. LlamaParse uses VLM-powered agentic document understanding to handle complex layouts, tables, charts, and handwritten text at scale.
@jerryjliu0: We pride ourselves on building document processing that is not only accurate and cheap, but massively scalable to milli…
LlamaParse now offers latency metrics for Parse, Extract, and Classify jobs, providing queue time, processing time, and total latency breakdowns. This helps users monitor and scale their document processing.
@jerryjliu0: LiteParse is the best open-source, model-free document parser for AI agents. Run it over over 50+ document types, and i…
LlamaIndex releases liteparse-server, a self-hosted, model-free HTTP API for parsing diverse document types with high spatial fidelity and privacy preservation.
@jerryjliu0: We've built the following document retrieval endpoints into LlamaParse: * Hybrid search (grep + vector search) * File g…
LlamaParse introduces new document retrieval endpoints including hybrid search, file grep, file find, and file read, aiming to improve agentic retrieval quality over unstructured documents.