How to build a reliable RAG system
Ingestion, chunking, hybrid retrieval, evaluation and source-aware answers.
Request this guide ↗Practical AI knowledge,
without the hype.
A growing collection of technical guides, decision frameworks and industry-specific use cases. Articles can be published progressively as original bilingual content.
Planned practical guides
RAG vs fine-tuning
How to choose based on knowledge freshness, behavior, cost and maintenance.
Request this guide ↗Cloud vs on-premise LLMs
A practical comparison of privacy, control, latency, cost and operations.
Request this guide ↗When do multi-agent systems make sense?
Responsibilities, routing, memory and the cases where one workflow is better.
Request this guide ↗Evaluating an LLM application
Quality datasets, groundedness, retrieval metrics, latency and cost.
Request this guide ↗A practical ML learning roadmap
From Python and statistics to projects, neural networks and production basics.
Explore coaching ↗AI by industry
Consulting & professional services
Document intelligence, internal knowledge assistants, research acceleration and proposal workflows.
SaaS & customer service
Product copilots, support agents, onboarding automation and semantic search.
E-commerce & retail
Segmentation, recommendations, product enrichment and demand intelligence.
Construction & industry
Business-data structuring, document processing, recommendation and knowledge capture.
Free resources in preparation
RAG project checklist
The questions to answer before choosing a model or vector database.
LLM benchmark template
A reusable framework for comparing quality, latency, privacy and cost.
ML learning roadmap
A structured progression from fundamentals to applied projects.
AI use-case canvas
A one-page tool for connecting a business problem with data and measurable value.
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