AdvancedИнженерия
Advanced Prompt Engineering
Deep dive into prompt engineering techniques: chain-of-thought, few-shot, self-consistency, tree-of-thoughts, ReAct, meta-prompts, XML structuring, adversarial testing, and automated prompt optimization. Applies to all top LLMs: Claude, GPT-4, Gemini.
5modules
14lessons
390 mintotal time
Developers and AI engineers building reliable LLM pipelinesaudience
Module 1
Foundations of Systematic Prompting
The LLM mental model, prompt structuring (XML/markdown, roles), and balancing zero-shot vs few-shot.
Module 2
Reasoning Techniques
Chain-of-thought and self-consistency, tree-of-thoughts, ReAct — how to make the model reason reliably.
Module 3
Instruction Design Patterns
Roles and personas, output constraints, and meta-prompts — how to precisely control model behavior.
Module 4
Reliability & Evaluation
Self-consistency in production, adversarial robustness, automated prompt-quality evaluation.
Module 5
Optimization & Scale
Automated prompt optimization (DSPy) and production practices: versioning, A/B, cost, drift monitoring.