ChatGPT & LLMs: A Practical Starter's Guide to Language Chains
summary
Shows live code using OpenAI + LangChain, Weaviate-style vectors, lsmith logging and Nemo guardrails to build concrete features: a batch sentiment labeler that classifies comments, a job-title synonym generator, a RAG Q&A over LangChain docs that returns sourced passages, a function-called Knowledge Graph extractor and an agent that issues SQL queries. Demonstrates LangChain abstracts model APIs and retrieval-augmented generation (RAG), reports instability (seed/temperature give nondeterministic outputs), cost tradeoffs (GPT‑4 ≈10x GPT‑3.5) and that prototypes work but need prompt tuning, chunking and verification to avoid hallucination.
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chapters (20)
- 0:00Introduction to LLMs and chains
- 1:09Use cases for large language models
- 4:16Introduction to L chain framework
- 8:08Controlling model creativity with parameters
- 13:33Why use L chain over raw APIs
- 14:57Building a chatbot with L chain
- 18:53Reliability of system prompts
- 23:12Sentiment analysis with LLMs
- 27:47Why frameworks like L chain are needed
- 28:10Generating training data with LLMs
- 32:11Introduction to chains in L chain
- 34:26Handling conversation history
- 39:52Retrieval Augmented Generation explained
- 50:12Vector embeddings and semantic search
- 1:00:30Function calling with structured outputs
- 1:06:42Agents and tools in L chain
- 1:10:34Challenges with vector embeddings
- 1:22:17Preventing hallucinations in LLMs
- 1:31:54Open source vs proprietary LLMs
- 1:36:34Q&A session
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