Intermediate Track

Few-Shot Prompting: Teaching AI by Example

Learn how to use few-shot prompting to dramatically improve AI output quality by showing the model exactly what you want through examples.

XML Tags & Delimiters: Structure Your Prompts Like a Pro

Learn how to use XML tags and delimiters to clearly separate instructions from data in your prompts — a technique that dramatically reduces errors on complex tasks.

Chain of Thought Prompting: Make AI Reason Step by Step

Chain of Thought (CoT) prompting forces AI to show its reasoning before answering — dramatically improving accuracy on logic, math, analysis, and multi-step tasks.

Avoiding Hallucinations: Keep AI Grounded in Facts

Learn what causes AI hallucinations and the specific prompting techniques that dramatically reduce fabricated facts, fake citations, and confidently wrong answers.

Constrained Generation: Force Structured Output

Learn how to make AI models reliably output JSON, XML, CSV, and other structured formats — essential for integrating AI into real applications and workflows.

System Prompts: Giving AI Standing Instructions

System prompts let you set persistent rules, persona, and context that apply to every message in a conversation. Learn how to write them effectively and when they change everything.

Prompting With Long Documents and Large Context

Pasting a 50-page document and asking 'what do you think?' rarely works. Learn how to structure prompts for long-form content, extract what matters, and work around context limits.

Multimodal Prompting: Images, Files, and Mixed Content

Modern AI models can see, read files, and process multiple input types at once. Learn how to structure prompts that work with images, documents, data files, and mixed content effectively.

Retrieval Augmented Generation (RAG): Ground Your AI in Real Data

RAG connects an LLM to an external knowledge base so it answers from facts rather than memory. Learn how RAG works, when to use it, and how to prompt effectively in RAG systems.

Self-Consistency: Get Better Answers by Sampling Multiple Reasoning Paths

Self-consistency generates multiple chain-of-thought responses and takes the majority vote. Learn how it dramatically improves accuracy on reasoning tasks and when to use it.

Generate Knowledge Prompting: Let the Model Teach Itself Before Answering

Generate Knowledge Prompting has the LLM produce relevant facts and context before answering a question — dramatically improving accuracy by giving the model a better foundation to reason from.

Reflexion: Teach AI to Learn from Its Own Mistakes

Reflexion is a technique where an LLM evaluates its own output, identifies what went wrong, and generates an improved response — a powerful self-correction loop for complex tasks.

Prompt Testing and Evaluation

Learn how to test prompts systematically — building golden sets, running regression tests, and measuring prompt quality before deploying to production.

Structured prompting with JSON schemas

Getting LLMs to output valid, structured JSON reliably — using JSON Schema as a contract, constrained generation modes, and error-handling patterns.

Working with Vision Models

Learn how to prompt multimodal AI models effectively — analyzing images, charts, screenshots, documents, and diagrams with Claude, GPT-4o, and Gemini.