Full-Stack AI Engineer
Full-Stack AI Engineers build the product layer on top of AI capabilities — connecting LLM APIs, RAG pipelines, and agent systems to user-facing interfaces. Their English work involves writing product specifications for AI features, documenting prompt versioning strategies, discussing cost trade-offs with engineering managers, and communicating AI system limitations to non-technical stakeholders. This path covers the intersection of web engineering and AI product development.
Topics covered
- LLM API integration
- Streaming UIs
- RAG pipelines
- Prompt engineering & versioning
- AI cost management
- Graceful degradation
Vocabulary spotlight
4 terms every Full-Stack AI Engineer should know in English:
An LLM output pattern where tokens are sent incrementally to the client as they are generated, rather than waiting for the full completion
"Streaming response reduced perceived latency from 8 seconds to near-instant for users."
An architecture that retrieves relevant documents from a knowledge base and includes them in the LLM prompt context to improve accuracy and reduce hallucination
"Without RAG, the model hallucinated product prices; adding retrieval grounded it to actual data."
Treating prompts as software artifacts with version control, changelogs, and evaluation before deployment
"Prompt versioning let us A/B test two system prompts and roll back when v3 degraded quality."
Designing an AI feature to fall back to a reduced-functionality or non-AI behaviour when the LLM is unavailable or producing low-confidence output
"If confidence is below 0.6, we gracefully degrade to showing the traditional search results."
📚 Vocabulary Reference
Key terms organised by category for Full-Stack AI Engineers:
LLM Integration
RAG & Retrieval
Prompt Engineering
Product & Cost
Recommended exercises
Real-world scenarios you'll practise
- Explaining a streaming response latency trade-off to a product manager who wants instant results
- Writing a design document for a RAG pipeline that serves 50,000 users daily
- Presenting an AI cost optimisation proposal: batching, caching, and model tier selection
- Communicating why the AI feature sometimes gives wrong answers and how you're mitigating it