AI Architecture Pattern Selector
Answer a few questions about your use case and get a recommended AI architecture pattern — RAG, GraphRAG, NL-to-SQL, Fine-Tuning, Agentic, or Hybrid — with trade-off analysis.
Your Use Case
Primary data type
Latency target
Auditability need
Regulatory sensitivity
Knowledge base size
Budget sensitivity
Additional requirements
Configure your requirements and click Select Pattern
Your recommended architecture pattern will appear here
All Architecture Patterns
Ground LLM answers in your own documents
Internal knowledge bases · Document Q&A
Traverse relationships, not just similarity
Research assistants · Biomedical / pharma
Query your databases in plain English
Business intelligence · Analytics chatbots
Bake domain knowledge into model weights
Classification / extraction at scale · Narrow-domain chat (legal, medical, code)
Let the LLM plan, act, and loop
Autonomous research agents · Code generation and execution
Best of both — docs and databases
Enterprise knowledge assistants · Platforms with both documents and databases
Architecture Selection Principles
- Match pattern to data type first. Unstructured text → RAG family. Structured databases → NL-to-SQL. Mixed → Hybrid. Entity networks → GraphRAG.
- Latency kills complexity. Real-time requirements rule out GraphRAG, Agentic, and most Hybrid approaches. Fine-tuning or cached RAG are the only paths to sub-500ms LLM responses.
- Regulated decisions need deterministic audit trails. NL-to-SQL produces SQL. RAG produces cited chunks. Agentic produces reasoning traces — harder to audit. Fine-tuning produces nothing traceable.
- Agentic is not a universal upgrade. Multi-step agent loops are expensive, slow, and hard to debug. Use them only when the task genuinely requires planning and tool composition.
- Start with RAG, add complexity only when it fails. Most enterprise knowledge Q&A problems are solved well by RAG. Add a semantic layer, graph, or agent capability only when you can measure a specific gap.