A Practical Starter's Guide to building LLM based projects
summary
Hands‑on demo that builds from a single OpenAI API prompt to a working pipeline using LangChain, showing: programmatic calls to models (GPT family and smaller “nano” models) for jokes and sentiment on three comments, a Retrieval‑Augmented Generation flow that loads a docs page and succeeds at summarizing it, vector embeddings for document similarity, function calling/structured output via Pydantic, agents (Wikipedia + Python interpreter) and a SQLite SQL‑agent; shows LangSmith tracing (noted ~$0.05 run), hallucination cases, token/cost tradeoffs and practical mitigations.
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chapters (19)
- 0:00LLM project building intro
- 1:04Simple prompts to LangChain
- 3:54Tokens and model selection
- 11:24Joke teller with LangChain
- 16:40Why LangChain matters
- 18:08LLM low-hanging use cases
- 22:23LLMs for training data
- 25:13Retrieval Augmented Generation
- 28:47Vector embeddings limits
- 36:58Conversation history handling
- 49:20Function calling and outputs
- 57:37Monitoring with LangSmith
- 1:00:55Knowledge graphs and schemas
- 1:02:22Building agents with tools
- 1:08:43Database agents and SQL
- 1:13:21Vector embeddings limits
- 1:19:50Managing hallucinations
- 1:23:24LLM project success tips
- 1:28:24Q&A on security
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