Your team wastes hours digging through PDFs, FAQs, and spreadsheets just to answer one question?
Here's the truth: You don't need developers or coding skills to fix this. This two-part course teaches you to build a complete RAG (Retrieval-Augmented Generation) system in Flowise that reads YOUR documents—PDFs, Word files, CSVs, even websites—and answers questions with pinpoint accuracy grounded in your actual business data, not generic internet information.
Learn RAG's two-phase workflow (Indexing + Retrieval), set up Flowise Document Store, configure Document Loaders for FAQ files, and apply Recursive Text Splitters with 1000-char chunks and 200-char overlap
Objective: Transform raw business documents into properly chunked, AI-ready knowledge segments ready for vectorization
Assemble Tool Agent + ChatOpenAI (GPT-4.1) + Buffer Memory + Document Store Retriever into one chatflow, fine-tune Top K (4–6) and Temperature (0.2–0.7), then deploy via embed widget, API, or share link
Objective: Launch a production-grade assistant that answers real queries with source attribution, conversation memory, and edge-case handling
Assemble Tool Agent + ChatOpenAI (GPT-4.1) + Buffer Memory + Document Store Retriever into one chatflow, fine-tune Top K (4–6) and Temperature (0.2–0.7), then deploy via embed widget, API, or share link
Objective: Launch a production-grade assistant that answers real queries with source attribution, conversation memory, and edge-case handling
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Total value
1000
HKD
You Pay = Only
498
HKD
One Time Payment
All Time Acess
/mo
$800 HKD
