Who Is This Course For?

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content creators

needing instant, accurate content answers for students

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customer support managers

seeking 50% reduction in repetitive query handling

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operations managers

unifying scattered documentation into intelligent response systems

Here's How It Works

Document Setup Mastery

Configure your Flowise document store to ingest PDFs, websites, and databases seamlessly

RAG Architecture Assembly

Connect retrievers, embeddings, LLMs, and memory components using visual drag-and-drop workflows

Memory & Context Engineering

Implement conversation memory and record managers for coherent, context-aware responses

Production Deployment

Launch your tested RAG assistant with proper chunking strategies and retrieval optimization

Meet Your Trainer

NextMaven AI | Tim Chan
Tim Chan
Founder of Growth Marketer Academy | NextMaven

In prev. venture, he helped MNC, Fortune 500, listed companies in HK & US and many regional fundraised startups on growth, lead generation, digital marketing, PR, content marketing etc.

He took various roles in marketing & startup related organizations before Curator of Facebook Community Leadership Circles: HKLead of Marketing Tech Sub-com, Cyberport Alumni HK Chapter Lead of Tech in Asia & CMX HubPresident, CUHK Alumni Entrepreneur AssociationHe published marketing & startup related articles on HuffingtonPost, Tech in Asia, e27, HK01, Economic Digest, StartupBeat, PressLogic, INSIDE etc.

Step By Step Breakdown

This is NOT going to be a "ONE-SIZE-FITS-ALL" Approach
like every other course out there teaching how to build AI bots.

Instead, we're going to provide production-ready Flowise templates specifically engineered for business knowledge automation

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Phase 1:
Foundation & Architecture


Setup your Flowise environment and understand RAG component relationships through hands-on building.
Objective:
Master the five core RAG components and their interconnections for reliable knowledge retrieval systems
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Phase 2:
Build Your Assistant


Construct your first production RAG chatflow with document ingestion, vector storage, and LLM integration
Objective:
Create a fully functional AI assistant that accurately retrieves and synthesizes your business knowledge
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Phase 3:
Optimize & Deploy


Fine-tune retrieval accuracy, implement conversation memory, and deploy your assistant for real-world use
Objective:
Launch a production-grade system handling complex queries with 95% accuracy and contextual awareness

In just 1 module of action-packed content

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Configure document stores for automatic knowledge ingestion and updates
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Deploy production RAG systems handling 100+ queries daily
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Troubleshoot and optimize retrieval quality using proven techniques

Join RAG with Flowise now and automate your knowledge base

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FAQs

Got Questions? We’ve Got You Covered!

Do I need programming experience to build a RAG system?

Absolutely not! Flowise is a visual no-code platform. If you can drag and drop icons, you can build production RAG systems. Every technical concept is explained in plain language.

How quickly can I have a working AI assistant?

Most students deploy their first functional RAG assistant within 3 hours. By the end of the weekend, you'll have a production-ready system handling real queries.

What exactly is RAG and why should I care?

RAG (Retrieval-Augmented Generation) lets AI access YOUR specific documents to answer questions accurately. Instead of generic AI responses, you get precise answers from your knowledge base.

How much will I save by building this myself?

Custom RAG development typically costs $5,000-$15,000. Support staff answering repetitive questions costs $3,000+/month. You'll build the same solution for free in one weekend.

What types of documents can my RAG system handle?

PDFs, Word docs, websites, CSVs, databases — virtually any text-based knowledge source. We show you how to connect multiple sources into one intelligent system.

Can this replace my customer support team?

It handles 60-80% of repetitive queries automatically, freeing your team for complex, high-value interactions. Think augmentation, not replacement.

Will my RAG assistant give wrong or hallucinated answers?

We teach specific techniques to minimize hallucination: proper chunking strategies, retrieval verification, and source attribution. Your assistant only answers from YOUR documents.

How does this compare to just using ChatGPT?

ChatGPT doesn't know YOUR business. RAG systems retrieve exact information from your documents, providing accurate, specific answers about your products, policies, and procedures.

What ongoing costs are involved after building?

Flowise can run locally for free. Cloud deployment costs $20-50/month. LLM API costs depend on usage but typically under $30/month for small teams.

Is there support if I get stuck?

You get lifetime access to course materials, future updates, and our step-by-step implementation guide. The bonus walkthrough ensures you never get stuck.

Can I build multiple RAG assistants for different purposes?

Absolutely! Once you understand the framework, you can build specialized assistants for sales, support, training, documentation — any knowledge domain you manage.

Will I get a certificate?

Yes! You’ll earn a completion certificate to showcase your new skillset.

Still have questions?

We'll get back to you in 1-2 business days