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

Enterprise AI hiring at large IT organisations such as Infosys 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 Infosys hiring tracks and the topics most worth preparing — with links to our full, detailed answer sets.

Infosys hires freshers through drives and HackWithInfy, into roles such as Systems Engineer (application development/testing), Specialist Programmer (SP) and Digital Specialist Engineer (DSE) — the latter two being higher-package, specialised tracks covering AI/ML, Cloud and Data Science. AI/GenAI evaluation shows up in SP/DSE and project interviews, where strong Python plus LLM, RAG, vector-database and Agentic-AI understanding is expected alongside DSA and cloud fundamentals.

Top 10 AI / RAG / Agentic questions Infosys 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

Infosys-style scenario questions

Productionize an LLM/RAG feature (SP/DSE angle)

Build the full RAG pipeline (ingestion → chunking → embeddings → vector search → prompt assembly), expose it via FastAPI, add evaluation and guardrails, and deploy with basic monitoring — the engineering-led focus SP/DSE interviews probe.

Design an enterprise knowledge assistant

Ingest proprietary docs → embed → store in a vector DB → retrieve with RAG → answer with an LLM, with metadata-based access control, citations and audit logging.

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 Infosys ask RAG and Agentic AI questions?

For AI/GenAI-oriented roles (especially Specialist Programmer and Digital Specialist Engineer), RAG and Agentic AI concepts are increasingly assessed. This guide reflects commonly-tested topics, not an official Infosys list.

What are the Infosys SP and DSE roles?

Specialist Programmer (SP) and Digital Specialist Engineer (DSE) are Infosys's higher-package specialised fresher tracks covering AI/ML, Cloud and Data Science, typically via HackWithInfy or targeted drives.

Which frameworks should I learn for Infosys AI interviews?

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