RAG Explained Through a Real Project
How retrieval-augmented generation works, explained by building one small real project end-to-end.
Not scheduled yet — no date, time or fee fixed. The most-voted topic is hosted next.
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Suggested mentor
Ajay Prajapat
AI Systems Architect
15+ years of experience · Ex-Apple
About mentor & skills Visit siteinnovatewithajay.com
About the suggested mentor
Ajay Prajapat
AI Systems Architect
- 15+ years of experience
- Ex-Apple
AI Systems Architect with 15+ years of experience, including building production GenAI systems — RAG pipelines, AI agents, prompt engineering and GenAI architecture. His sessions are hands-on: expect real workflows, live builds and honest trade-offs, not slideware.
Skills & expertise
- AI-assisted Development
- Full-stack Development
- AI Agents
- RAG & MCP
- System Design
- Project Reviews
- Prompt Engineering
- GenAI Architecture
A suggested match for this topic. The trainer and session details will be confirmed when the webinar is scheduled.
Learn this topic with a mentor
Don't want to wait for the webinar? Pick how you'd like help. Nothing is paid now — you see the fee before anything is booked.
- Bring your own work — code, campaign, report or plan
- Mentor matched to this topic
- Time, format and fee confirmed by email first
What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1The problem RAG solves: models don't know your data
- 2Walking through a real project's data pipeline
- 3Retrieval: finding the right chunks
- 4Augmentation: building the prompt with context
- 5Generation and citing sources
- 6Lessons learned and common failures
Who it's for
- • Developers and technical learners
- • Product people wanting to understand RAG
You'd leave with
- An end-to-end understanding of RAG
- Awareness of common failure modes
- A reference project structure
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