Is Fine-Tuning Still Needed? LLMs, RAG, & LoRA
Martin Keen explains how LLM fine tuning compares to RAG, LoRA, and generative AI techniques, outlining when custom model training is still necessary in modern AI workflows.
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LLMs
All digests tagged LLMs
Why RAG Solutions Fail with Complex Documents & Vector Databases
Learn how RAG solutions fail with complex documents and contradictory laws. We discuss practical techniques like clarification loops and robust document management to handle ambiguity in AI systems.
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Omnigent: Composition, Control, and Collaboration for AI Agents
Omnigent introduces the concept of a 'meta-harness' layer, allowing developers to compose AI agents by abstracting away model changes. The discussion covers agent debate, tokenomics as DevOps, and the critical need for stateful memory.
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How to Go From Data Scientist to AI Engineer (I Did This)
This roadmap guides transitioning from Data Science to AI Engineering, emphasizing the shift from notebooks to structured Python projects using FastAPI, Pydantic, Docker, and PostgreSQL for production-ready systems.
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Building an AI-Powered Personal Companion Mobile App - Gerald Versluis - NDC Copenhagen 2026
Learn how to build an AI-powered companion app using .NET MAUI. This session covers integrating LLMs, handling multimodal input (voice/image), and architecting for model agnosticism across platforms.
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