| 001. Introduction to Large Language Models (LLMs) and Prompt Engineering Introduction.en.srt | 1.7 KB | ||
| 001. Introduction to Large Language Models (LLMs) and Prompt Engineering Introduction.mp4 | 11.5 MB | ||
| 001. Introduction to Large Language Models (LLMs) and Prompt Engineering Summary.en.srt | 1 KB | ||
| 001. Introduction to Large Language Models (LLMs) and Prompt Engineering Summary.mp4 | 7.5 MB | ||
| 001. Topics.en.srt | 409.6 B | ||
| 001. Topics.mp4 | 2.9 MB | ||
| 002. 1.1 What Are Large Language Models.en.srt | 10.8 KB | ||
| 002. 1.1 What Are Large Language Models.mp4 | 120.7 MB | ||
| 002. 2.1 Introduction to Semantic Search.en.srt | 17.9 KB | ||
| 002. 2.1 Introduction to Semantic Search.mp4 | 63.3 MB | ||
| 002. 3.1 Introduction to Prompt Engineering.en.srt | 31.8 KB | ||
| 002. 3.1 Introduction to Prompt Engineering.mp4 | 121.3 MB | ||
| 002. 4.1 Introduction to Retrival Augmented Generation (RAG).en.srt | 16.3 KB | ||
| 002. 4.1 Introduction to Retrival Augmented Generation (RAG).mp4 | 54.9 MB | ||
| 002. 5.1 InputOutput Validation.en.srt | 10.9 KB | ||
| 002. 5.1 InputOutput Validation.mp4 | 45.6 MB | ||
| 003. 1.2 Popular Modern LLMs.en.srt | 25.5 KB | ||
| 003. 1.2 Popular Modern LLMs.mp4 | 97.6 MB | ||
| 003. 2.2 Building a Semantic Search System.en.srt | 20.6 KB | ||
| 003. 2.2 Building a Semantic Search System.mp4 | 148.8 MB | ||
| 003. 3.2 Working with Prompts Across Models.en.srt | 5.4 KB | ||
| 003. 3.2 Working with Prompts Across Models.mp4 | 22.4 MB | ||
| 003. 4.2 Building a RAG Bot.en.srt | 22.2 KB | ||
| 003. 4.2 Building a RAG Bot.mp4 | 113.5 MB | ||
| 003. 5.2 Batch Prompting + Prompt Chaining.en.srt | 9.2 KB | ||
| 003. 5.2 Batch Prompting + Prompt Chaining.mp4 | 40.1 MB | ||
| 004. 1.3 Applications of LLMs.en.srt | 4.5 KB | ||
| 004. 1.3 Applications of LLMs.mp4 | 16.2 MB | ||
| 004. 2.3 Optimizing Semantic Search with Cross-Encoders and Fine-Tuning.en.srt | 35.4 KB | ||
| 004. 2.3 Optimizing Semantic Search with Cross-Encoders and Fine-Tuning.mp4 | 161.3 MB | ||
| 004. 3.3 Building a Retrieval-Augmented Generation Bot with ChatGPT and GPT-4.en.srt | 43.1 KB | ||
| 004. 3.3 Building a Retrieval-Augmented Generation Bot with ChatGPT and GPT-4.mp4 | 171.3 MB | ||
| 004. 4.3 Using Open Source Models with RAG.en.srt | 20.3 KB | ||
| 004. 4.3 Using Open Source Models with RAG.mp4 | 103.9 MB | ||
| 004. 5.3 Chain-of-Thought Prompting.en.srt | 17.9 KB | ||
| 004. 5.3 Chain-of-Thought Prompting.mp4 | 59.9 MB | ||
| 005. 4.4 Expanding into AI Agents.en.srt | 22.4 KB | ||
| 005. 4.4 Expanding into AI Agents.mp4 | 116.8 MB | ||
| 005. 5.4 Preventing Prompt Injection Attacks.en.srt | 5.8 KB | ||
| 005. 5.4 Preventing Prompt Injection Attacks.mp4 | 21.5 MB | ||
| 006. 5.5 Assessing an LLM's Encoded Knowledge Level.en.srt | 5.9 KB | ||
| 006. 5.5 Assessing an LLM's Encoded Knowledge Level.mp4 | 27.8 MB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 52 total files | |||
Introduction to Large Language Models (LLMs) and Prompt Engineering
https://WebToolTip.com
Released 5/2025
By Sinan Ozdemir
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + subtitle | Duration: 5h | Size: 1.51 GB
Learn how to use and launch large language models (LLMs) like GPT, Llama, Claude T5, and BERT and design prompts for optimal AI workloads.
Introduction to Large Language Models (LLMs) and Prompt Engineering guides you to launch LLMs like GPT, Llama, Claude, T5, and BERT at scale. It presents a step-by-step approach to building and deploying LLMs, with real-world case studies to illustrate the concepts. It also covers how to begin your LLM journey with prompt engineering with optimal instruction placements and prompting across models. The video works toward building a Retrieval-Augmented Generation (RAG) system with LLMs. It fills a gap in the market by providing a guide to using LLMs and will be a valuable resource for anyone looking to use LLMs in their projects.
The companion GitHub repository for this course is athttps://github.com/sinanuozdemir/quick-start-guide-to-llms
About the Instructor
Sinan Ozdemir is founder and CTO of LoopGenius, where he uses state-of-the-art AI to help people create and run their businesses. He has lectured in data science at Johns Hopkins University and authored multiple books, videos, and numerous online courses on data science, machine learning, and generative AI. He also founded the recently acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. Sinan most recently published Quick Guide to Large Language Models, 2nd Edition, and launched a podcast audio series, AI Unveiled. Ozdemir holds a master’s degree in pure mathematics from Johns Hopkins University.
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