14-Day Plan: LLM → LangChain → RAG → Build

AI Interview Mastery14 days

AI Interview Important Basics

Your Fast 14-Day Plan

Follow this sequence on Learnixo. Check off lessons in each path, then practice in the simulator.

Full GenAI interview courseQuick Learnixo reviewPractice in simulator

Days 1–3Foundations

Learn

• LLM basics
• Prompt engineering
• AI agents
• MCP basics

Study on Learnixo

• LLM deep dive (interview)
• LLM providers & selection
• Prompt engineering (Part 1)
• Agents & frameworks
• MCP for .NET

Days 4–6Orchestration & toolsLangChain ↓

Learn

• Semantic Kernel
• LangChain
• Tool calling
• Memory
• Workflows

Study on Learnixo

• What is LangChain?
• LangChain agents (concepts)
• LangChain RAG chain
• Agents, SK, LangGraph & MCP
• Tool calling scenarios
• Agent memory
• LangGraph workflows
• LangChain pipelines (interview)

Days 7–9RAG & searchRAG ↓

Learn

• RAG
• Embeddings
• pgvector
• Azure AI Search

Study on Learnixo

• RAG interview (Part 1)
• RAG interview (Part 2)
• Azure OpenAI guide
• Hybrid search
• RAG in production

Days 10–14Build & ship

Build

• Chatbot
• AI agent
• Document search assistant

Study on Learnixo

• Build a chatbot (OpenAI)
• PharmaBot: FastAPI backend
• PharmaBot: vector search / RAG
• Production RAG pipeline
• Pharmacy / copilot use cases
• Healthcare AI interview guide
• AI safety & guardrails
• LLM evaluation
• Practice in simulator

Quick Learning Resources

Start here

Check off lessons · practice in simulator when done

Learnixo lessons and interview articles in order — check off as you go, then practice in the simulator.

LangChain

Chains, agents, tools, memory, LCEL, RAG — full interview path on Learnixo.

LangChain Mastery courseDeep dive section ↓

1. 1.What is LangChain?
2. 2.Core primitives (models, prompts, chains, tools)
3. 3.LCEL overview
4. 4.Sequential chains
5. 5.Interview: multi-step pipeline
6. 6.How agents work
7. 7.Tool calling vs ReAct agent
8. 8.Types of memory
9. 9.Interview: choose the right memory
10. 10.Document loaders & chunking
11. 11.Vector stores
12. 12.Full RAG chain with LCEL
13. 13.Interview: production scenarios

Practice in simulator after this path

LangGraph

Graph state, nodes, cycles, checkpoints, multi-agent — interview-ready on Learnixo.

LangGraph agents course

1. 1.Why LangGraph beyond linear chains
2. 2.Nodes, edges, and state
3. 3.State schema
4. 4.Your first StateGraph
5. 5.Conditional edges & routing
6. 6.Cycles and retry loops
7. 7.Checkpointing & persisted state
8. 8.Human-in-the-loop
9. 9.Supervisor multi-agent pattern
10. 10.Interview: agent flow design
11. 11.Interview: LangGraph architecture
12. 12.Compare: LangGraph vs LangChain vs SK

Practice in simulator after this path

Semantic Kernel

Plugins, planners, C# agents, Azure copilots — your .NET interview edge on Learnixo.

AI agents in C# course

1. 1.What are AI agents & how SK implements them
2. 2.AI agents guide (tools & reasoning)
3. 3.Interview: SK, LangGraph, CrewAI, MCP
4. 4.Interview: tool calling scenarios
5. 5.Interview: agent memory
6. 6.Interview: agent patterns
7. 7.CrewAI vs LangChain vs LangGraph

Practice in simulator after this path

MCP

Model Context Protocol — servers, tools, .NET integration, enterprise security on Learnixo.

GenAI interview course (MCP module)

1. 1.MCP, RAG & agents explained
2. 2.MCP servers for .NET & Azure
3. 3.Interview: agents, SK, LangGraph & MCP

Practice in simulator after this path

RAG & vector search

Embeddings, pgvector, Azure AI Search, hybrid retrieval — Days 7–9 on Learnixo.

RAG systems courseDeep dive section ↓

1. 1.Interview: RAG (Part 1)
2. 2.Interview: RAG (Part 2)
3. 3.Vector stores overview
4. 4.PharmaBot vector search
5. 5.Hybrid search
6. 6.RAG evaluation: recall & golden sets
7. 7.RAG in production
8. 8.Building a production RAG pipeline
9. 9.LangChain RAG chain

Practice in simulator after this path

Azure OpenAI

Deployments, models, safety, enterprise patterns — interview guides on Learnixo.

Azure OpenAI course

1. 1.Azure OpenAI: GPT-4o, embeddings, production
2. 2.Interview: OpenAI vs Azure vs Claude vs Gemini
3. 3.LLM cost breakdown & routing
4. 4.PharmaBot Azure cloud patterns

Practice in simulator after this path

CrewAI, evals & guardrails

Multi-agent crews, LLM evaluation, safety — close the loop before shipping.

GenAI interview course

1. 1.CrewAI vs LangChain vs LangGraph
2. 2.Interview: CrewAI production
3. 3.Interview: AI safety & guardrails
4. 4.Interview: LLM evaluation
5. 5.Interview: fine-tuning vs RAG
6. 6.PharmaBot security & compliance

Practice in simulator after this path

RAG & vector search

Days 7–9 — embeddings, pgvector, Azure AI Search

1. User question
2. Embed query
3. Vector / hybrid search
4. Retrieve chunks
5. LLM answer

• Embeddings
• Chunking
• Vector DB / pgvector
• Azure AI Search
• Hybrid search
• Eval (recall@K)

1. 1.Interview: RAG (Part 1)
2. 2.Interview: RAG (Part 2)
3. 3.Vector stores overview
4. 4.PharmaBot vector search
5. 5.Hybrid search
6. 6.RAG evaluation: recall & golden sets
7. 7.RAG in production
8. 8.Building a production RAG pipeline
9. 9.LangChain RAG chain

Practice in simulator after this path

LangChain

Framework essentials for enterprise AI interviews

Build & ship

Days 10–14 — projects to mention in interview

Interview cheat sheet

One-page reminders — expand each topic before interview day

Print cheat sheet

Internal AI assistant

• •RAG over docs → vector search (Azure AI Search / pgvector)
• •Azure OpenAI + tool calling + REST APIs
• •RBAC, tenant isolation, audit logs

Reduce hallucinations

• •RAG grounding, cite sources in prompt
• •Structured outputs / JSON schema, temperature 0
• •Eval set + regression tests

Integrate with internal systems

• •APIs + MCP or function-calling layer
• •Least-privilege per tool, RBAC on every call
• •Audit trail for tool invocations

LangChain vs LangGraph vs SK

• •LangChain: linear chains, agents, RAG pipelines
• •LangGraph: state graph, cycles, checkpoints, HITL
• •Semantic Kernel: .NET plugins, Azure enterprise copilots

RAG pipeline

• •Chunk → embed → store → retrieve → synthesize
• •Hybrid = vector + BM25 (drug names, SKUs)
• •Measure recall@K on golden queries

MCP

• •Standard tool/server protocol for agents
• •Auth per server, network isolation, audit tools

What you should be able to explain

Senior interview talking points

Biggest advantage you have

How to use this plan

Use Quick Learning Resources below — each technology has an ordered Learnixo review path (lessons + interview articles) for LangChain, LangGraph, Semantic Kernel, MCP, and Azure OpenAI. Work through the four phases in order. Days 4–6 include a dedicated LangChain section (chains, agents, tool calling, memory, RAG) plus Semantic Kernel for .NET/Azure shops. Each block links to interview articles and course lessons on Learnixo. After Days 10–14, practice out loud in the Interview Simulator using the AI / GenAI topics in the same sequence.

For the full structured course (providers, RAG, agents, pharmacy use cases, production guardrails), continue with GenAI & LLM Interviews.