42. How to Update LLM Knowledge: RAG, Fine-Tuning & More Explained In Hindi скачать в хорошем качестве

42. How to Update LLM Knowledge: RAG, Fine-Tuning & More Explained In Hindi

Large Language Models are powerful, but their knowledge is static. In this video, we dive deep into what "knowledge" actually means for an LLM—from the facts stored in its parameters to the patterns learned during pre-training. We also explore the critical challenge of the "knowledge cutoff" and provide a comprehensive guide on the six primary ways to update or augment an AI's internal database. Whether you are building AI agents or just curious about how ChatGPT stays current, this video covers the essential techniques you need to know. What you will learn: What constitutes "Knowledge" in an LLM. The difference between parametric knowledge and external context. Retrieval-Augmented Generation (RAG): Connecting to external APIs and databases. Fine-Tuning: Using PEFT, LoRA, and QLoRA for efficient retraining. Prompt Engineering: Providing dynamic context at inference time. Memory-Augmented Systems: Implementing long-term memory for AI agents. Knowledge Injection: Using tools like Wikipedia APIs and enterprise knowledge bases. If you found this breakdown helpful, please Like the video and Subscribe for more deep dives into AI architecture and LLM development!