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#267 - Step-by-Step: Build a Real AI Project with Next.js & RAG
Manage episode 502352388 series 3399111
What does it actually mean to be an “AI Engineer”?
Honestly—not much. The title is overloaded and vague.
But what is meaningful right now is knowing how to build real projects with AI that go beyond toy chatbots and portfolio fluff.
In this episode, I walk you through the exact project I’ve been building at two different AI startups: a Retrieval Augmented Generation (RAG) app. You’ll learn how to:
- Scrape and store content in a vector database
- Use embeddings to turn your text into something a model can understand
- Stream responses back to your frontend with Next.js + TypeScript
- Reduce hallucinations and add structured, reliable outputs
- Understand why this is the skillset employers are actually hiring for right now
👉 Check out the repo and code from this episode here: https://www.parsity.io/ai-with-rag
If you’ve been wondering how to actually learn AI engineering skills that matter in 2025, this is the place to start.
Shameless Plugs
🧑💻 Join Parsity - Become a full stack AI developer in 6-9 months.
✉️ Got a question you want answered on the pod? Drop it here
Zubin's LinkedIn (ex-lawyer, former Googler, Brian-look-a-like)
Розділи
1. What is an AI Engineer? (00:00:00)
2. Understanding RAG Systems (00:05:29)
3. The Technical Stack Breakdown (00:10:25)
4. Building Your Vector Database (00:14:34)
5. Implementing the Chat Interface (00:18:07)
6. Structured Responses and Advanced Features (00:21:38)
7. Final Thoughts and Resources (00:24:38)
277 епізодів
Manage episode 502352388 series 3399111
What does it actually mean to be an “AI Engineer”?
Honestly—not much. The title is overloaded and vague.
But what is meaningful right now is knowing how to build real projects with AI that go beyond toy chatbots and portfolio fluff.
In this episode, I walk you through the exact project I’ve been building at two different AI startups: a Retrieval Augmented Generation (RAG) app. You’ll learn how to:
- Scrape and store content in a vector database
- Use embeddings to turn your text into something a model can understand
- Stream responses back to your frontend with Next.js + TypeScript
- Reduce hallucinations and add structured, reliable outputs
- Understand why this is the skillset employers are actually hiring for right now
👉 Check out the repo and code from this episode here: https://www.parsity.io/ai-with-rag
If you’ve been wondering how to actually learn AI engineering skills that matter in 2025, this is the place to start.
Shameless Plugs
🧑💻 Join Parsity - Become a full stack AI developer in 6-9 months.
✉️ Got a question you want answered on the pod? Drop it here
Zubin's LinkedIn (ex-lawyer, former Googler, Brian-look-a-like)
Розділи
1. What is an AI Engineer? (00:00:00)
2. Understanding RAG Systems (00:05:29)
3. The Technical Stack Breakdown (00:10:25)
4. Building Your Vector Database (00:14:34)
5. Implementing the Chat Interface (00:18:07)
6. Structured Responses and Advanced Features (00:21:38)
7. Final Thoughts and Resources (00:24:38)
277 епізодів
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