Every resource here comes from a real engagement. Guides, templates, and checklists extracted from production AI projects — not theoretical frameworks written by people who have never shipped.
Curated paths through our resource library — from beginner to practitioner.
From tokens to transformers: understand how large language models work, how to evaluate them, and how to run them cost-effectively.
Navigate EU AI Act obligations, security red-teaming, and data governance — everything required to deploy AI in regulated environments.
From readiness to roadmap: the complete journey from assessing where you are to building and defending an AI business case.
For engineers: build production AI systems — from MCP and agentic architecture to RAG and data strategy.
High-risk AI systems must comply by August 2026. Get prepared with our complete guide and 47-point compliance checklist.
Our most popular guides and templates
The definitive resource for understanding Europe's landmark AI regulation. Covers risk classification, technical requirements, penalties, and step-by-step compliance guidance. Updated for 2026.
25 min read
Read NowBuild retrieval-augmented generation systems that actually work in production. Covers architecture, chunking, embeddings, vector databases, retrieval strategies, and evaluation frameworks.
35 min read
Read NowEverything you need to build production AI agents. Covers ReAct, tool-use, and multi-agent architectures. Includes framework comparison (LangGraph, CrewAI, OpenAI Agents SDK), guardrails, evaluation, and deployment patterns.
40 min read
Read NowThe most comprehensive guide to Large Language Models: tokenization, transformer architecture, attention mechanisms, pretraining, RLHF, DPO, inference sampling, context windows, RAG, open-source models, quantization, and evaluation. 101 through expert.
60 min read
Read NowEverything about MCP: protocol architecture, transport layers (stdio, HTTP+SSE, Streamable HTTP), tools, resources, prompts, sampling, roots, security model, and full Python/TypeScript server implementation examples.
45 min read
Read NowProtect your AI systems from prompt injection, jailbreaks, data poisoning, and model theft. Covers OWASP LLM Top 10, adversarial testing methodologies, and defense-in-depth strategies for production AI.
35 min read
Read NowBuild a bulletproof AI business case. Includes cost modeling framework, ROI projections, risk quantification, stakeholder alignment templates, and a 12-month implementation timeline.
20 min read
Read NowThe definitive compliance checklist for EU AI Act. Covers system inventory, risk classification, documentation requirements, and implementation timeline. Used by our clients to achieve audit-ready compliance.
Read NowEverything you need to build production AI agents. Covers ReAct, tool-use, and multi-agent architectures. Includes framework comparison (LangGraph, CrewAI, OpenAI Agents SDK), guardrails, evaluation, and deployment patterns.
The most comprehensive guide to Large Language Models: tokenization, transformer architecture, attention mechanisms, pretraining, RLHF, DPO, inference sampling, context windows, RAG, open-source models, quantization, and evaluation. 101 through expert.
The definitive guide to open source AI: frontier models (Llama, Mistral, Qwen, DeepSeek), training frameworks, fine-tuning with LoRA/QLoRA, inference servers (vLLM, TGI, Ollama), vector databases, orchestration frameworks, and how to choose your stack.
Complete guide to teaching AI models new skills: supervised fine-tuning (SFT), LoRA/QLoRA parameter-efficient adaptation, RLHF, DPO, GRPO, model distillation, model merging (TIES, DARE), dataset preparation, and evaluation frameworks.
These guides cover the common patterns. Your situation has specific constraints — industry, tech stack, team, timeline. A 30-minute call is enough to map out what applies to you.