← Academy advanced track · master level
Prompt Engineer Pro.
From chain-of-thought to RLHF. Evals, fine-tuning and production system design.
Chain-of-thought & ReAct
Eval framework design
Fine-tuning & RLHF
Guardrails & security
Multi-agent systems
Cost-aware architecture
lesson plan · 12 lessons
Go deep.
01 02 03 04 05 06 07 08 09 10 11 12
Chain-of-thought prompting
Step-by-step thinking: how to make the model 'think'.
Few-shot & in-context learning
Teaching by example: selection, order and token budget.
Self-consistency & tree-of-thought
Generating multiple paths and picking the best one.
ReAct & planning
Reasoning + acting: how agents plan.
Eval frameworks
LLM-as-judge, rubric-based evaluation, A/B.
Automatic regression tests
Seeing what breaks when a prompt changes.
Fine-tuning: when and how
Data collection, hyperparameters and cost.
RLHF fundamentals
Aligning models with human feedback.
Guardrails & security
Jailbreaks, prompt injection and defense layers.
Multi-agent orchestration
Coordinating multiple agents.
Cost-aware architecture
Router, caching and hybrid model strategies.
Case study: a production AI product
An AI product end to end — design, deploy, measure.