LLM Mastery for Enterprise AI Engineering
Intermediate
Design and implement real systems · 8 tutorials · 25-35 min each
Produce a defensible enterprise AI readiness packet: use-case brief, system choice, prototype, eval suite, deployment plan, governance evidence, and release decision.
Datasets, Training, and Data Governance
SFT data, instruction tuning, preference data, synthetic data, curation, formatting, and enterprise data cards.
Fine-Tuning with LoRA, QLoRA, DPO, and RLHF
How to customize models responsibly and prove the tuned model is better than the baseline.
Inference and Optimization
KV cache, Flash Attention, speculative decoding, serving, batching, GPU memory, and latency-quality tradeoffs.
Local AI Ecosystem
llama.cpp, Ollama, vLLM, MLX, Hugging Face, Unsloth, Axolotl, PEFT, and TRL.
RAG, Memory, and Access Control
Retrieval-augmented generation, vector databases, chunking, memory systems, semantic search, and enterprise RAG security gates.
Agents, Workflows, and Tool Safety
Prompting, system prompts, tool calling, agents, multi-agent workflows, browser agents, and enterprise tool-use controls.
Model Types and Selection
Vision-language models, small language models, dense vs MoE, coding models, reasoning models, and fit-for-purpose selection.
LLM Engineering Patterns and Anti-Patterns
Production design patterns, anti-patterns, decision tables, and real-world scenarios across the full LLM lifecycle.