LLM Wikis take a different approach to agent memory. Instead of just retrieving raw chunks like RAG (and then forgetting them), the agent actually curates what it learns: it reads new information, reconciles it against what it already knows, and rewrites its own knowledge into clean, current pages — much like a human maintaining a living wiki.

In this session, Izma Aziz, Senior Software Engineer - Generative AI and LLMs at Data Science Dojo, breaks down how LLM Wikis actually work — and why they matter for agents that need to remember.

Here’s what we’ll cover: What LLM Wikis are, and how an agent maintains its own knowledge base How they differ from RAG, file search, and chat memory Where they pay off — fewer tokens, lower cost, cleaner knowledge How knowledge gets created and updated, in a live build What makes agent memory reliable in production If you’re building or maintaining LLM-powered agents and want a real alternative to RAG for long-term memory, this one’s for you.

August 5, 2026 | 1:00 PM PT Register here: https://hubs.la/Q04rd0bf0

#LLMWikis #AIAgents #AgentMemory #GenerativeAI #LLM #LangGraph #DeepAgents #RAG #AIEngineering #MachineLearning #ArtificialIntelligence

LLM Wikis: How AI Agents Build a Second Brain That Never Forgets

Source: https://datasciencedojo.com/event/llm-wikis-ai-agent-memory/?utm_term=llm_wikis_how_to_build_a_second_brain_webinar&utm_campaign=5277859-Community%20Events&utm_content=383484836&utm_medium=social&utm_source=twitter&hss_channel=tw-1318985240 Izma Aziz is a Senior Software Engineer in Generative AI and LLMs at Data Science Dojo, where she leads an AI engineering team and works hands on with the latest agentic concepts as they move from research into practice. Her work spans multi-agent architectures, RAG pipelines, and the memory and context engineering that keeps agents reliable at scale, built primarily with LangChain and LangGraph. She is a Microsoft Certified Azure Solutions Architect Expert and Azure AI Engineer Associate, and a regular speaker and writer on generative and agentic AI.