The ai-study-helper-v2 project tested prompts, query expansion, corpus-grounded facets, auditing, reranking, and section routing for exhaustive topic coverage in Agentic RAG. Adding more tools, turns, and queries did not reliably reveal every semantic facet, while a term-by-term full-text search recovered all three regression probes but retrieved 511 of 526 blocks. The project is therefore preserving block retrieval and moving toward precompiled, source-traceable LLM Wiki evidence maps as a possible way to organize and complete topic coverage.
What problem it solves
Complex financial questions require researchers to break them into manageable tasks, gather current market information, and evaluate the resulting analysis. The project addresses this by automating research planning, tool-driven data collection, and iterative self-validation.
Key features
Intelligent task planning — Complex queries are automatically decomposed into structure…
What problem it solves
OpenPencil lets users describe UI in natural language and see it appear on an infinite canvas, then modify existing designs by selecting elements and chatting. It also lets multiple AI agents work on different sections of complex pages in parallel while keeping `.op` files human-readable, Git-friendly and diffable.
Key features
Prompt-to-canvas generation — Describe a UI in …
What problem it solves
Teams building retrieval-augmented AI systems need to extract useful knowledge from complex, heterogeneous documents and ground generated answers in traceable evidence. They also need an orchestrated workflow that can combine document understanding, chunking, retrieval, reasoning, and citations.
Key features
Deep document understanding — Extract knowledge from unstructured d…
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