Wipro AI Interview Questions on RAG & Agentic AI (2026) — Freshers Guide

Enterprise AI hiring at large IT organisations such as Wipro increasingly expects freshers to understand LLMs, Retrieval-Augmented Generation (RAG), AI agents, Agentic AI frameworks, MCP, vector databases and embeddings in addition to Python, DSA and cloud fundamentals. This concise guide covers the Wipro hiring tracks and the topics most worth preparing — with links to our full, detailed answer sets.

Wipro hires freshers through Elite NTH (national talent hunt), Turbo (higher-package specialised) and the WILP work-integrated program, with skilling via TalentNext. AI/GenAI evaluation appears in Turbo and project interviews for roles such as AI Engineer, GenAI Engineer and Full Stack AI Developer, where — beyond Python, DSA, SQL and cloud basics — LLMs, RAG, vector databases and Agentic AI for enterprise automation are increasingly assessed.

Top 10 AI / RAG / Agentic questions Wipro freshers should prepare

1. What is RAG and why do enterprises use it?
   Retrieval-Augmented Generation retrieves relevant documents from a knowledge base before the LLM answers, grounding responses in current, private company data and reducing hallucinations — which is why it is the default pattern for enterprise AI. Full answers: see our RAG interview guides linked below.
2. What is Agentic AI vs Generative AI?
   Generative AI produces content in a single response; Agentic AI plans, uses tools, executes multi-step tasks, observes results and iterates toward a goal. Enterprises use agents to automate workflows, not just draft text.
3. What are embeddings and a vector database?
   Embeddings are numeric vectors capturing text meaning; a vector database (FAISS, Pinecone, Chroma, Weaviate, Qdrant, Milvus) stores and searches them by similarity — the retrieval backbone of RAG.
4. What is chunking and why does it matter?
   Chunking splits large documents into smaller pieces (often with overlap) before embedding, improving retrieval accuracy and keeping context within the LLM window.
5. RAG vs fine-tuning — when to use which?
   Use RAG for knowledge that changes or must be cited; use fine-tuning (LoRA/QLoRA) for specialized style/behaviour. Many enterprise systems combine both.
6. What is MCP and A2A?
   MCP (Model Context Protocol) standardises how models connect to tools and data; A2A (Agent-to-Agent) lets multiple agents collaborate and share tasks.
7. What is LangChain / LangGraph / CrewAI?
   LangChain builds LLM apps; LangGraph builds stateful, graph-based agent workflows with loops and human-in-the-loop; CrewAI orchestrates teams of role-based agents.
8. How do you reduce hallucinations?
   Ground with RAG, rerank retrieval, use structured prompts and validated outputs, restrict tool permissions, and add human approval for high-risk actions.
9. How do you evaluate a RAG or agent system?
   RAG: faithfulness, answer relevancy, context precision/recall (RAGAS), Precision@K, Recall@K. Agents: task success rate, tool-call correctness, steps-to-completion, latency and cost.
10. How do you secure an enterprise AI agent?
    Authentication, role-based access control, secrets in a vault, least-privilege sandboxed tools, PII redaction, audit logging, and defences against prompt injection.

These are summaries. For full detailed answers, work through our canonical guides:

• Top 50 RAG interview questions for freshers — fundamentals first
• Top 45 RAG interview questions (freshers & experienced) — advanced & production
• Top 60 Agentic AI interview questions — agents, tools, MCP, LangGraph, evaluation
• Top 60 AI & ML interview questions — fundamentals, deep learning, MLOps

Wipro-style scenario questions

Build an AI assistant for IT infrastructure support

Retrieve internal documentation with RAG → analyse incident details → recommend troubleshooting steps → trigger approved automation via tools → escalate unresolved issues to engineers → generate an incident summary, with audit logging.

Automate cloud provisioning with an AI agent

Accept a natural-language request → validate requirements → generate Infrastructure as Code → provision resources → verify the deployment → produce logs and outputs, with human approval for production.

Freshers preparation roadmap

Prepare in this order: Python → DSA (basic) → SQL → ML basics → LLM fundamentals → prompt engineering → embeddings → vector databases → RAG → LangChain → LangGraph → AI agents → MCP/A2A → one cloud (AWS/Azure). Build one end-to-end RAG project and one simple AI-agent project with GitHub documentation to discuss in interviews. Follow the full plan in our Fresher-to-Hired 2026 roadmap and browse live roles in the 2026 fresher jobs hub.

📞 Want mock interviews or a structured AI learning path? Call or WhatsApp +91 96660 19191 / +91 99496 16388, or email info@cloudsoftsol.com. Explore our AI & Cloud training and full course catalogue.

Frequently Asked Questions

Does Wipro ask RAG and Agentic AI interview questions?

For AI-engineering and GenAI roles (especially the Turbo track), RAG and Agentic AI concepts are increasingly relevant because they underpin enterprise AI and automation. This guide reflects commonly-assessed topics, not an official Wipro list.

What are Wipro Elite, Turbo and WILP?

Elite NTH is Wipro's national fresher hiring program, Turbo is the higher-package specialised track (AI/cloud/full-stack), and WILP is the Work Integrated Learning Program combining work with a degree.

Which frameworks should I learn for Wipro AI interviews?

LangChain, LangGraph, LlamaIndex, CrewAI and AutoGen, plus strong Python, SQL, ML basics and one cloud platform.