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🧠Stay grounded in vetted knowledge. 💸 Control spend without bespoke fine-tuning cycles. 🚀 Ship a trustworthy copilot with the engineers you already have.
Verified retrieval keeps responses tethered to audited sources.
Semantic search over your corpus is leaner than repeated fine-tuning cycles.
Engineers can assemble a RAG stack in weeks using existing models and tooling.
Crafted by Zachary Proser, senior AI/ML infrastructure engineer and Developer Experience Engineer at WorkOS. Looking for hands-on help? Explore services.
The query text is received and prepared for semantic processing. This plaintext input will be transformed into a numerical representation that enables meaning-based search.
See inputs, outputs, and understand the entire flow as data moves through each stage
The user enters a question in plaintext
The user query is the starting point of every RAG pipeline. This plaintext question captures the user's intent and will be transformed through multiple stages to retrieve relevant information.
Well-formed queries lead to better retrieval. The same query processed through RAG will yield consistent, grounded answers—unlike pure LLM responses that can vary or hallucinate.
The query text is normalized (trimmed, standardized) but otherwise kept as-is at this stage. It will be converted to a vector representation in the next step, enabling semantic search rather than keyword matching.
In the next step, this query will be passed to an embedding model that converts it into a dense vector—a mathematical representation of its semantic meaning.
Follow the exact system I refined while leading RAG architecture at Pinecone. The premium tutorial includes the notebook that preprocesses your data, the Next.js app that ships to Vercel, and direct email support when you hit edge cases.