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LLM fundamentals
Tokens, context windows, safety, and how models are trained.
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Prompt engineering
Few-shot, chain-of-thought, structured outputs, and evaluation.
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Building with APIs
Integrate OpenAI, Anthropic, and open models in production.
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RAG systems
Chunking, embeddings, vector stores, and retrieval quality.
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Evals & safety
Benchmarks, red-teaming, guardrails, and monitoring.
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Planning, function calling, and multi-step workflows.
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Multimodal AI
Vision, audio, and unified models for real-world inputs.
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LLM ops
Latency, cost, caching, and deployment patterns.
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AI product strategy
Use cases, roadmaps, and shipping AI features users trust.
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