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Free GenAI Resources
130+ tutorials curated into downloadable guides, cheatsheets, and templates. Get up to speed on RAG, AI Agents, Prompt Engineering, and more.
GenAI Getting Started Guide
A curated learning path through 130+ tutorials, from fundamentals to production-ready AI agents. Know exactly what to learn and in what order.
What's inside:
- Structured learning path from beginner to advanced
- Curated tutorial recommendations for each skill level
- Architecture decision flowcharts for common GenAI patterns
- Links to hands-on Jupyter notebooks for every concept
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Top 10 Prompt Engineering Templates
Battle-tested prompt templates extracted from the Prompt Engineering book. Copy-paste ready for ChatGPT, Claude, and any LLM.
What's inside:
- Chain-of-thought reasoning template
- Few-shot learning with structured examples
- Role-based system prompt frameworks
- Output formatting and validation patterns
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AI Agent Architecture Cheatsheet
Visual reference for building AI agents, from single-agent loops to multi-agent orchestration. Covers ReAct, Plan-and-Execute, and more.
What's inside:
- ReAct agent pattern with tool integration
- Plan-and-Execute architecture diagram
- Multi-agent communication patterns
- Memory and state management strategies
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RAG Implementation Checklist
Step-by-step checklist for building production RAG systems, covering chunking, embedding, retrieval, and evaluation strategies.
What's inside:
- Document processing and chunking strategies
- Embedding model selection guide
- Retrieval optimization techniques
- Evaluation metrics and testing framework
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Related Blog Posts
Jev Explained: The Model That Decides Without Writing a Word
What TypeSafe's new System One model is, how it decides without writing, and where it will fail you.
What a Word Actually Does Inside an AI Image Model, and How to Write Prompts That Land
One experiment on one real photograph, and the prompt rule that falls out of how diffusion models are trained.
Vibe Training - Auto Train a Small Language Model for Your Use Case
Most AI products enforce their policies by piping every message through a frontier model with the rule in the prompt. A framework called BARRED replaces that duct tape with a small, dedicated, fine-tuned classifier.
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