Pruning RAG context down to what the answer actually needs
Kapa.ai has developed a method to prune 68% of irrelevant context from their retrieval-augmented generation (RAG) system while maintaining 96% recall accuracy. Their AI assistants utilize a retrieval API to access and process information from extensive product knowledge bases, including technical documentation and support threads.
kapa.ai
8 min
7/6/2026
Is Grep All You Need? How Agent Harnesses Reshape Agentic Search
Recent advancements in Large Language Model (LLM) agents allow for complex workflows where models autonomously retrieve information, utilize tools, and reason over large datasets. Retrieval-augmented generation (RAG) is increasingly adopted in agentic search systems to enhance task completion.
arxiv.org
2 min
6/9/2026
Gemini API File Search is now multimodal
Gemini API's File Search tool now supports multimodal data and custom metadata for building retrieval-augmented generation (RAG) systems. The update includes page citations to enhance grounding and transparency, enabling better organization of text and visual content.
blog.google
2 min
5/10/2026
Pruning RAG context down to what the answer actually needs
Kapa.ai has developed a method to prune 68% of irrelevant context from their retrieval-augmented generation (RAG) system while maintaining 96% recall accuracy. Their AI assistants utilize a retrieval API to access and process information from extensive product knowledge bases, including technical documentation and support threads.
kapa.ai
8 min
7/6/2026
Gemini API File Search is now multimodal
Gemini API's File Search tool now supports multimodal data and custom metadata for building retrieval-augmented generation (RAG) systems. The update includes page citations to enhance grounding and transparency, enabling better organization of text and visual content.
blog.google
2 min
5/10/2026
Is Grep All You Need? How Agent Harnesses Reshape Agentic Search
Recent advancements in Large Language Model (LLM) agents allow for complex workflows where models autonomously retrieve information, utilize tools, and reason over large datasets. Retrieval-augmented generation (RAG) is increasingly adopted in agentic search systems to enhance task completion.
arxiv.org
2 min
6/9/2026
Pruning RAG context down to what the answer actually needs
Kapa.ai has developed a method to prune 68% of irrelevant context from their retrieval-augmented generation (RAG) system while maintaining 96% recall accuracy. Their AI assistants utilize a retrieval API to access and process information from extensive product knowledge bases, including technical documentation and support threads.
kapa.ai
8 min
7/6/2026
Is Grep All You Need? How Agent Harnesses Reshape Agentic Search
Recent advancements in Large Language Model (LLM) agents allow for complex workflows where models autonomously retrieve information, utilize tools, and reason over large datasets. Retrieval-augmented generation (RAG) is increasingly adopted in agentic search systems to enhance task completion.
arxiv.org
2 min
6/9/2026
Gemini API File Search is now multimodal
Gemini API's File Search tool now supports multimodal data and custom metadata for building retrieval-augmented generation (RAG) systems. The update includes page citations to enhance grounding and transparency, enabling better organization of text and visual content.
blog.google
2 min
5/10/2026
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