Build production AI systems: OpenAI and Anthropic APIs, advanced RAG pipelines, fine-tuning with LoRA/QLoRA, evaluation with RAGAS and LLM-as-judge, LangGraph agentic workflows, cost optimization with caching and model routing, and LLM security and guardrails.
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OpenAI and Anthropic APIs, token economics, system prompts, chain-of-thought, structured outputs, and prompt patterns that actually work in production
Build retrieval-augmented generation pipelines, vector databases, agentic tool use, function calling, and multi-step reasoning systems
When to fine-tune vs prompt engineer, full fine-tuning vs LoRA, QLoRA for 4-bit fine-tuning on consumer hardware, PEFT library, and preparing training datasets
Evaluation frameworks (LLM-as-judge, RAGAS, BLEU/ROUGE), cost optimization with caching and model routing, latency optimization, observability, and production LLM architecture
LangChain Expression Language (LCEL), chains, LangGraph for stateful multi-step agents, tool use, human-in-the-loop, and building reliable agentic systems
Prompt injection attacks, jailbreaks, PII detection, content filtering, output validation, rate limiting LLM endpoints, and building safe AI products