The AI Backend Stack
You're a backend developer. You know databases, APIs, and how to ship features. Now everyone wants AI in their app. The good news? You don't need to become an ML engineer. You need to learn how to integrate AI into your existing stack.
You want to add AI features to your backend without a PhD. We'll focus on practical integration, not model training.
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What's Changed for Backend Devs?
Before 2023, adding "AI" meant hiring ML engineers, training models, and managing infrastructure. Now? You make API calls.
- Hire ML team
- Collect training data
- Train custom models
- Deploy on GPU clusters
- Months of work
- Call an API
- Send a prompt
- Get response
- Ship feature
- Hours of work
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The Modern AI Backend Stack
These three components combine to enable RAG (Retrieval-Augmented Generation)
The Three Core Components
The "brain" - processes text, generates responses, understands context
Stores text as numbers, enables "search by meaning"
Documents, knowledge base, database content to ground AI responses
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When to Use AI vs Traditional Code
This is the most important decision you'll make. AI is not a replacement for code - it's a tool for specific problems.
| Task | Use | Why |
|---|---|---|
| Calculate order total | Traditional code | Deterministic, exact math needed |
| Summarize customer feedback | LLM | Unstructured text, needs understanding |
| User authentication | Traditional code | Security-critical, must be exact |
| Classify support tickets | LLM | Fuzzy categories, natural language |
| Search products by name | Traditional search | Exact matching works fine |
| Search "shoes for hiking" | Vector search | Intent-based, semantic matching |
If you can write clear if/else rules for it, use traditional code. If the rules are fuzzy or the input is natural language, consider AI.
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Real AI Features You Can Build
Chat with your docs, answer questions from knowledge base
Find content by meaning, not just keywords
Product descriptions, email drafts, summaries
Categorize tickets, detect sentiment, route requests
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What You'll Learn in This Course
By the end, you'll build a Document Q&A bot - a complete RAG application that answers questions from your own documents.
Key Takeaways
- AI integration is about APIs - not training models
- Use AI for fuzzy problems - natural language, classification, generation
- Use traditional code for exact problems - math, auth, CRUD
- The stack is simple - LLM API + Vector DB + Your Data
Next up: Calling LLM APIs - your first AI integration.