Step-by-step technical guides on building, stress-testing, and optimizing production-grade AI architectures.
A cascading routing pipeline designed to minimize token costs. It uses a faster, cheaper LLM for initial triage, only routing ambiguous cases to a heavy, expensive model for deep analysis.
A standard Retrieval-Augmented Generation pipeline optimized for enterprise support. It pairs dense text embeddings with Claude 3.5 Sonnet to deliver context-aware, low-latency streaming responses.
A deterministic data extraction pipeline. By utilizing native tool calling rather than prompt engineering, it forces the LLM to output strict JSON schemas for reliable database ingestion.
A high-traffic optimization pattern. It routes incoming requests through a semantic cache layer, defaulting to an ultra-fast LLM fallback only when a cache miss occurs, drastically reducing P50 latency.
A precision-focused RAG architecture. It broadens initial vector retrieval and applies a dedicated reranker model to filter noise, maximizing the contextual relevance passed to the generation model.
An advanced, 9-node agentic workflow that combines semantic caching with an orchestrator LLM to route complex customer queries to specific API tools before final synthesis.
A rigorous, 9-node RAG pipeline. It queries a primary, highly-specific vector index first. If confidence is low, it falls back to a broad archive index, reranking results to ensure hallucination-free legal synthesis.
A quality-assured translation pipeline utilizing 8 nodes. It attempts localization with a high-speed LLM, enforces formatting via tool calls, and routes failures to a heavy-duty reasoning model for self-correction.
Get notified when we release new blueprint templates and architectural benchmarks.