This report explains why hallucinations happen from training objectives and inference behavior, compares benchmark evaluation with practical evaluation, and outlines what RAG, verification, citations, and tools can and cannot fix.
#llm
A practical guide to the difference between pretraining, fine-tuning, prompting, RAG, tool use, and agents, framed by what each one changes and when to use it.
A practical introduction that breaks LLMs down into tokenization, embeddings, Transformer self-attention, pretraining, next-token prediction, inference, and context length.
A literature-grounded report on Wittgensteinian language games, intentionality, and recent philosophical work on large language models.
A practical report reframing LLM-based AI coding as an engineering discipline for increasing the probability that software changes finish correctly.
We will organize the basics of the SECI model and tacit knowledge, and summarize from a deployment perspective what generative AI is good at and what tends to distort summarization, translation, and knowledge management.
An integrated report that connects the history of AI research, LLM, symbolic grounding, Ontology, Graphiti, and MCP to practical decisions for utilizing organizational knowledge.