ChatGPT, LLMs, and LangChains - A Beginner's Guide
summary
Covers building end-to-end LLM systems with concrete tools: OpenAI chat API, LongChain chains that pipe prompts and outputs, connectors to PostgreSQL and ElasticSearch, and a vector DB rerank stage. Shows quick wins like sentiment analysis on millions of reviews and a travel assistant that fails on “elevator” until fixed with retrieval augmented generation and vector embeddings, explains chunking PDFs, hybrid keyword+vector search, JSON structured output, non‑deterministic testing, cost tradeoffs and when to run private models.
AI-written summary · about
chapters (13)
- 0:00Introduction to Large Language Models
- 1:07Understanding the Challenges of AI
- 1:52Practical Applications of LLMs
- 2:53Implementing Sentiment Analysis
- 3:09Exploring LongChain Framework
- 3:43Choosing the Right Model and API
- 5:39Handling Unexpected AI Behavior
- 8:31Retrieval Augmented Generation
- 9:40Vector Embeddings and Hybrid Search
- 13:09Challenges in AI Implementation
- 16:41Testing and Open Source Considerations
- 18:41Cost and Risk Management
- 19:41Conclusion and Q&A
related talks
01h 50mChatGPT & LLMs: A Practical Starter's Guide to Language Chains
01h 34mA Practical Starter's Guide to building LLM based projects
01h 59mLLM applications with LangChain
58mDemystifying GenAI: Build a ChatGPT App with Vector Store
41mBuild Next-Generation Applications with Language Models using LangChain and Python
28mAPIs and GenAI : Your Perfect Combo with LLM-Powered Frameworks.
01h 00mUnleashGPT: Mastering the Implementation of Large Language Models
18mLLMs and GenAI: A Guide to Building Your Own RAG Model for Document Interaction