15 minutesHow We Build Effective Agents: Barry Zhang, Anthropic
Design simpler agent loops with clear stopping rules, task boundaries and human control points.
AdvancedAutomations
Developing an LLM: Building, Training, Finetuning
Sebastian Raschka. Sebastian Raschka's slower walkthrough of where fine-tuning sits in the broader LLM training pipeline — instruction tuning, classification fine-tuning, parameter-efficient methods, and the trade-offs the article calls out before recommending LoRA. Good calibration before you start, especially if your team is debating whether fine-tuning is even the right step.
AI Expert note
Model names, pricing and capabilities change quickly. Use this for the decision pattern, then verify current model behavior before adopting it.
What you should get from this
Place fine-tuning inside the broader training pipeline and decide when it is better than prompting or RAG.
Watch or know first
General ML literacy; it's a conceptual walkthrough, so no code-along setup is needed.
Last reviewed: May 18, 2026
Watch next
Continue through the same learning path with the next curated companion videos.
15 minutes
190 minutesLangGraph Complete Course for Beginners – Complex AI Agents with Python
You can build a LangGraph agent with typed state, conditional routing, checkpoints and tool use, and feel where the explicitness pays off.
AdvancedAutomations
8 minutesContext Rot: How Increasing Input Tokens Impacts LLM Performance
Understand how long context can fail under ambiguity and distractors, then design tests around that risk.
AdvancedPrompt Engineering