💡 The story discusses AI-assisted search-based research, which likely involves tools for retrieval-augmented generation (RAG) or search frameworks (e.g., LlamaIndex-style systems), fitting the 'tools' category under Engineering.
💡 The story likely addresses YAML usage in AI development workflows or tools (common in frameworks like LangChain/LlamaIndex), fitting the tools category for development tools & frameworks.
💡 The story inquires about AI development tools (LangChain, LlamaIndex) for PDF parsing in the context of RAG, which aligns with the tools category under Engineering.
💡 The story addresses solving the out-of-context chunk problem for Retrieval-Augmented Generation (RAG), a core technique used in AI development tools and frameworks like LangChain or LlamaIndex.
💡 The story focuses on optimizing a local LLM voice assistant using RAG, which relies on tools like vector databases and frameworks (e.g., LlamaIndex) that are classified under the tools category.
💡 The story inquires about a local-first AI solution for PDF search, which typically relies on tools like LlamaIndex, LangChain (RAG frameworks) or vector databases—all explicitly listed under the 'tools' category.
💡 The article is about systematically improving Retrieval-Augmented Generation (RAG), a technique heavily reliant on AI development tools and frameworks like LangChain or LlamaIndex, which falls under the 'tools' category in Engineering.
💡 The story introduces Cognita, an open-source RAG framework for modular applications, which aligns with the 'tools' category covering AI development frameworks like LangChain and LlamaIndex.
💡 The story focuses on LLM ingestion and preprocessing, which are core functionalities supported by AI development tools (e.g., LangChain, LlamaIndex) for data preparation in LLM workflows.
💡 The story introduces LlamaCloud and LlamaParse from LlamaIndex, which are AI development tools/frameworks for building LLM applications, fitting the 'tools' category under Engineering.