In this hands-on course, you will learn to build and utilize a fully local, privacy-preserving AI research assistant powered by LLMs such as those hosted on Ollama or LM Studio. Named the Local Deep Researcher, this system enables autonomous, iterative research on any given topic by cycling through intelligent querying, information retrieval, summarization, and reflection—all without needing cloud APIs or external services.
You’ll explore the full pipeline, including:
Generating intelligent search queries using an LLM
Gathering and parsing web search documents
Creating and updating concise summaries of findings
Reflecting on current summaries to identify knowledge gaps
Refining search queries to address those gaps
Producing a clean, markdown-based final research report with source attribution
Key Learning Outcomes:
Deploy and configure LLMs locally using Ollama or LM Studio
Understand autonomous agentic loops for research tasks
Design and implement research workflows for summarization and knowledge refinement
Automate the generation of structured, referenced reports
Who This Course Is For:
Researchers and students looking for AI-powered research tools
Developers building intelligent personal knowledge agents
Privacy-conscious professionals needing offline research support
Enthusiasts interested in agentic AI, LLM automation, and local-first tools
Tools & Tech Stack:
Python
DeepSeek / Other local LLMs
Ollama or LM Studio
LangChain (optional)
Markdown for report generation
By the end of the course, you'll have your own fully functioning Local Deep Researcher capable of producing structured reports with iterative reasoning—all on your local machine.