Interview prep · Marathahalli
Django or Flask plus RAG, LLM and Agentic AI — the 2026 interview combo
Why Python web frameworks plus GenAI is the job description companies post in 2026, and how to prep at MufaSoft Bangalore.
Why this combination is everywhere
Product teams already have Django or Flask services. In 2026 they add RAG and agents on top of those services — not a separate Python notebook. Job descriptions therefore ask for both: ship an API, then put retrieval and an agent behind it.
What a strong answer looks like
You should be able to design: auth, a documents table, an embedding job, a retrieve endpoint, an LLM generate step with citations, and an optional LangGraph agent that can search, call tools, and stop. You should know cost, latency, and eval — not only prompt poetry.
- Django vs Flask vs FastAPI for an LLM gateway.
- Sync Celery/RQ jobs for embedding vs request-time embedding.
- How you version prompts and models without breaking the API.
Prep path at MufaSoft
Course work covers the stack. Interview support is mocks on that exact combo. Book a demo, tell us your current role, and we map a 4–8 week plan for working professionals.
FAQ
- I know Django but not GenAI. Where do I start?
- We start with RAG on a small internal doc set, then LLM eval, then one agent graph. Django stays the delivery layer.
- Is this only for freshers?
- No. Most Python + GenAI interview support students are working professionals switching from support, QA, or backend.
Train at MufaSoft in Marathahalli
Next step: Python + GenAI interview support, or talk to a counsellor in 5 minutes.