Generative AI in the real world
summary
Practical notes on deploying generative models: cost hinges on tokenization and GPT‑4 80k token context limits, pricing charges both input and output tokens, and Azure shows Azure throttling across data centers that can break production if unhandled. Mitigations used: caching, vector DBs, chunking for large docs, prompt chaining and fine‑tuning. Failures seen include hallucinations (e.g., miscounting "I think therefore I am") and prompt‑injection/jailbreak risks; a lyrics→song→video pipeline using LangChain orchestrator, Songer and Steve AI produces a decent result.
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chapters (12)
- 0:00Introduction to generative AI
- 1:53Defining models and architecture
- 4:41Pricing and cost management
- 8:23API limits and optimization
- 10:17Effective prompting techniques
- 13:30Understanding hallucinations
- 16:02Legal and ethical challenges
- 18:59Processing large data volumes
- 24:32Orchestration with LangChain
- 27:05Security vulnerabilities
- 31:29Securing AI applications
- 33:32Conclusion and final thoughts
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