You now have vocabulary for data, evaluation, ethics, and product integration. Generative AI builds on this foundation with transformers, prompting, RAG, fine-tuning, and safety for text and multimodal systems.
What you bring forward
- Leakage-aware evaluation and slice metrics
- Privacy, transparency, and human review habits
- Build vs buy and cloud governance thinking
- Understanding that LLMs are narrow systems with broad interfaces
Gen AI topics ahead
- Prompt design and system messages
- Retrieval-augmented generation over your documents
- Tool use and agents (with guardrails)
- Content safety, jailbreaks, and policy layers
- Cost/latency trade-offs of model tiers
Bridge exercise
bridge = {
"completed": "AI literacy track",
"next": "/gen-ai/intro",
"also_consider": ["/dsa/intro", "/scipy/intro"],
}
print("Next track:", bridge["next"])Practice: Review prior lessons, then explain key concepts aloud or in notes—interview readiness is verbal clarity, not memorizing APIs.
Important interview questions and answers
- Q: Why literacy before Gen AI?
A: Prompting without evaluation and safety basics creates confident wrong outputs at scale. - Q: RAG builds on what?
A: Data inventory, chunking, search indexes—data modeling and product lessons here.
Self-check
- Name three concepts you carry into Gen AI.
- What is one Gen AI topic listed for the next track?
Tip: Carry evaluation and ethics habits into Generative AI—prompting amplifies both good and bad process.
Interview prep
- Why literacy before Gen AI?
- Prompting without evaluation and safety scales confident errors.
- RAG builds on?
- Data inventory, chunking, and search—data modeling lessons here.