Alkemy Playground

See AI patterns in action.

This is where we open the hood. The Playground is Alkemy Cloud's interactive showcase of the AI architectures we recommend and implement for clients — built so you can explore how they work, not just read about them.

• Live architectures. Each pattern includes an interactive walkthrough showing exactly how data flows from query to answer.
• Real AI demos. Not mock-ups. Every demo runs a live LLM call so you see actual retrieval, ranking, and generation.
• Decision-ready. Pros, cons, and "when to use" guidance so you leave knowing which pattern fits your use case.

Not all RAG is created equal.

Retrieval-Augmented Generation (RAG) lets AI answer questions using your actual data — documents, knowledge bases, APIs — instead of relying on memorised training data. It is the most practical way to make LLMs accurate, current, and trustworthy in production.

But RAG is a family of architectures, not a single technique. The right pattern depends on your query complexity, corpus size, and accuracy bar — and choosing wrong costs months.

RAG is the SAFE bridge between AI potential and business reality.

• Sourced. Every answer traces back to specific chunks in your knowledge base. No black boxes.
• Always current. Update your KB and answers reflect the change instantly. No retraining required.
• Fact-grounded. RAG grounds LLM responses in your actual data — not memorised training sets.
• Enclosed. Your data stays under your control. No fine-tuning, no data leaving your perimeter.

The six core RAG patterns

• Naive RAG. Documents are chunked, embedded as vectors, and retrieved by similarity at query time.
• Advanced RAG. Adds query rewriting, broad retrieval, and cross-encoder re-ranking to improve precision.
• Modular RAG. A composable architecture with swappable components and routing logic per query type.
• GraphRAG. Uses knowledge graphs for multi-hop reasoning across connected entities.
• Corrective RAG. Self-evaluates retrieved chunk relevance and triggers fallback when quality is insufficient.
• Agentic RAG. An autonomous agent that decides when and what to retrieve, decomposes complex queries, and synthesises results.