Advanced

Tree of Thoughts & Self-Consistency

Explore branching reasoning paths and majority-vote strategies to dramatically improve accuracy on hard problems.

ReAct & Reflexion

Learn how interleaving reasoning with actions and self-reflection loops push model capabilities further.

Program-Aided Language Models

Combine natural language reasoning with executable code to solve math, data, and logic problems accurately.

Embeddings & Vector Stores

Understand how text becomes searchable vectors and how vector databases power semantic search.

Chunking Strategies

Learn how to split documents into chunks that maximize retrieval quality and minimize noise.

Building RAG Pipelines

Assemble end-to-end RAG systems with query routing, re-ranking, and answer synthesis.

Agent Prompting Fundamentals

Understand the architecture and prompting patterns behind effective AI agents.

Function Calling & MCP

Master structured function calling and the Model Context Protocol for reliable tool use.

Multi-Step Agent Workflows

Design and prompt complex agent workflows with planning, delegation, and error recovery.

Injection Attacks Explained

Understand how prompt injection works and why it is the #1 security risk in LLM applications.

Defensive Prompting

Build layered defenses into your prompts to resist injection and maintain intended behavior.

Red-Teaming Your Prompts

Systematically test your prompts against adversarial attacks to find and fix vulnerabilities.

Building Eval Suites

Create test suites that measure prompt quality across diverse inputs and edge cases.

Scoring Rubrics & Metrics

Design rubrics and select metrics that reliably measure what matters for your use case.

Regression Testing & Versioning

Track prompt versions, detect regressions, and maintain quality as prompts evolve.

APE & DSPy

Learn how Automatic Prompt Engineering and DSPy use AI to discover optimal prompts.

Meta-Prompting & Compression

Use meta-prompts to generate task-specific prompts and compress prompts without losing quality.