Technical Read Me: Token Math
To provide a transparent look into the operations of this AI-powered library, this section breaks down the fiscal reality of running a production-grade RAG (Retrieval-Augmented Generation) system.
Phase A: Ingestion (The Knowledge Sync)
When the "Sync" button is clicked, the system performs Semantic Chunking and Vector Embedding. Modern models like text-embedding-3-small are hyper-efficient, making a full site update cost less than a single SMS message.
Phase B: Inference (The Intelligent Search)
Every search query retrieves 3–5 most relevant "data chunks" (approx. 1,500 tokens of context). The LLM (Gemini 1.5 Flash) then synthesizes a response. Adaptive RAG ensures we skip expensive steps for simple queries.
As a Senior Specialist, I believe in Systemic Transparency. Design is never just about the UI—it is about the system's sustainability and unit economics.
Fiscal Transparency Report
The unit economics of a production-grade RAG pipeline.
Optimization Protocol
To ensure accuracy and prevent AI hallucinations, theorycraft.in uses a custom RAG-powered search bar. I designed a manual Sync Engine that semantically chunks and embeds my latest technical work, providing the LLM with grounded, real-time context.