Production — AI-Ops & Scaling

What you'll build

• Observability & Tracing with Langfuse
• Cost monitoring & Latency budgets
• Caching patterns (Semantic & Exact)
• Fine-tuning vs Prompting trade-offs

Lessons

1. Observability & Tracing in Production

   Building an AI app is easy; knowing why it failed in production is hard. Learn how to use tracing (Langfuse) to debug complex chains.

2. Cost Monitoring & Token Budgets

   AI bills can spiral out of control. Learn how to track every cent, implement token budgets, and predict your monthly burn.

3. Caching Patterns (Semantic & Exact)

   The fastest (and cheapest) LLM call is the one you don't make. Learn how to use Redis to cache responses and save thousands.

4. Fine-Tuning vs. Prompting

   When is a better prompt not enough? Learn the trade-offs between RAG, prompting, and training your own model weights.

5. Background Jobs & Async Processing

   Long-running AI tasks don't belong in a web request. Learn how to use BullMQ or Celery to handle AI processing in the background.

6. Prompt Security & Injection Defense

   Attackers will try to hijack your model. Learn how to defend against Prompt Injection and build safe AI applications.

7. Open-Source Models & Local Hosting

   You don't always need a paid API. Learn how to host models like Llama 3 or Mistral on your own hardware using Ollama and vLLM.