AI Engineering Resource Library
Every Technovids AI Engineering guide, career resource, RAG explainer, AI agents guide, MCP reference, LangChain framework guide, and training page — organised in one place. Whether you are just starting out or building production AI systems, this library has the right resource for your next step.
32 resources across AI engineering careers, RAG, AI agents, frameworks, and training. All free to read. Guides are updated to reflect 2026 tools and practices.
AI Engineering Career Guides
6 resources
AI Engineering Guide
The definitive overview of AI engineering — what it is, what AI engineers do, the skills required, tools used, career paths, and how the discipline compares to data science and ML engineering.
AI Engineering Roadmap
A stage-by-stage learning roadmap covering every skill, tool, and project milestone needed to go from beginner to job-ready AI engineer in 2026.
AI Engineer Salary India
Salary ranges by role, level, city, and company type for AI engineers in India. Covers AI Engineering Manager, MLOps, RAG, and agent specialisations with 2026 data.
AI Engineer Skills
Complete technical skill map for AI engineers — Python, prompt engineering, RAG, LangChain, LangGraph, agents, MCP, evaluation, deployment, and observability.
AI Engineer Projects
Portfolio project ideas with architecture guidance — covering RAG pipelines, document assistants, LangGraph agent workflows, and deployed FastAPI AI services.
AI Engineering Interview Questions
Complete AI engineering interview preparation guide — LLMs, RAG, vector databases, agents, LangChain, MCP, system design, deployment, project walkthroughs, and a 30-day prep plan.
RAG & Retrieval-Augmented Generation
4 resources
What is RAG?
Complete explainer on Retrieval-Augmented Generation — how it works, why it is used, the full pipeline from document loading to RAGAS evaluation, and enterprise use cases.
RAG vs Fine-Tuning
Side-by-side comparison of RAG and fine-tuning — best use cases, trade-offs, data requirements, production challenges, and a decision framework for choosing between them.
What is a Vector Database?
Complete guide to vector databases — embeddings, semantic search, vector search mechanics, tools comparison (Pinecone, Chroma, FAISS, pgvector), RAG integration, and best practices.
Production RAG System Architecture
Complete 13-layer production RAG architecture guide — data ingestion, chunking, embeddings, vector databases, retrieval, reranking, prompt orchestration, evaluation, monitoring, security, and deployment.
AI Agents & Agentic AI
3 resources
What Are AI Agents?
Comprehensive guide to AI agents — tools, memory, workflows, multi-agent systems, enterprise examples, and how agentic systems differ from simple LLM applications.
Agentic AI Explained
Deep explainer on agentic AI — what it means, how agentic workflows are designed, tool use, planning, multi-step reasoning, and real enterprise deployment examples.
LangGraph vs CrewAI
Production comparison of LangGraph and CrewAI — design philosophy, state management, observability, learning curve, and decision guidance for enterprise agent deployments.
Frameworks & Tools
4 resources
What is LangGraph?
Complete guide to LangGraph — stateful graph-based AI agent workflows, nodes, edges, state management, conditional routing, human-in-the-loop, RAG orchestration, and production agent best practices.
What is LangChain?
Complete guide to the LangChain framework — key components, how it powers RAG pipelines and AI agents, comparison with building from scratch, limitations, best practices, and skills needed.
What is MCP?
Full explainer on Model Context Protocol (MCP) — the open standard for connecting AI agents to tools, data sources, and APIs. Covers architecture, MCP servers, clients, and enterprise use cases.
LLM Prompt Engineering Guide
Practical guide to LLM prompt engineering — system prompts, prompt templates, structured outputs, RAG prompts, tool-calling instructions for agents, prompt evaluation, versioning, and production use.
LLMOps & Production AI Systems
3 resources
What is LLMOps?
Complete guide to LLMOps — the practices, tools, and workflows for building, deploying, monitoring, evaluating, and continuously improving production LLM applications.
LLMOps vs MLOps
Complete comparison of LLMOps and MLOps — lifecycle, data, evaluation, monitoring, deployment, cost, failure modes, and what AI engineers need to know.
How to Evaluate LLM Applications
Metrics, test datasets, RAG evaluation, agent evaluation, LLM-as-judge, production monitoring, and pre-launch checklists for production AI teams.
Training & Mentorship
10 resources
AI Engineering Course
Live instructor-led AI Engineering course — LangChain, RAG, LangGraph agents, MCP, deployment. 5 production projects. For individual developers building AI engineering skills.
Production AI Engineering
Corporate team programme — 5-day intensive or 8-week deep track. Covers RAG, LangGraph, MCP, observability, and deploying production AI systems at enterprise scale.
1:1 AI Engineering Mentorship
3-month personalised mentorship for developers transitioning to AI engineering roles. Portfolio projects, code review, career guidance, and job-search support.
Corporate AI Training
Customised AI training programmes for organisations — covering GenAI, ChatGPT, prompt engineering, data analytics, and AI automation for business teams.
RAG Training India
Corporate RAG training for developer teams building retrieval-augmented generation systems in production.
LangChain Training India
Corporate LangChain and LangGraph training for teams building production LLM applications.
Claude AI Training Course
Live Claude AI training course covering Claude Chat, Claude Code, connectors, MCP concepts, business productivity workflows by department, and developer AI adoption.
Free Claude Training
Free introductory Claude AI training session — basics, prompting, document analysis, productivity workflows. Beginner-friendly. Register to join the next available session.
ChatGPT Enterprise Training
ChatGPT Enterprise training for organizations — role-based workflows, Apps in ChatGPT, company knowledge, governance, responsible AI and enterprise rollout planning.
Claude Enterprise Training
Claude Enterprise training for teams — Claude Chat, Cowork, Claude Code, connectors, MCP, shared projects, governance and organizational adoption planning.
Enterprise AI and Business Tools Guides
2 resources
ChatGPT Apps and Connectors Guide
A complete guide to ChatGPT Apps and connectors — how they work, app types (file search, sync, deep research, custom/MCP), company knowledge, permissions, admin controls, security and enterprise use cases.
Claude Connectors Guide
A complete guide to Claude connectors — connector types (remote, desktop extensions, custom MCP), permission model, enterprise-managed auth, evaluation checklist, security considerations and responsible adoption.
Recommended AI Engineering Learning Path
Not sure where to start? Follow this sequence to build AI engineering knowledge systematically — from concept to production skill.
- 1
AI Engineering Guide
Understand the discipline, roles, and ecosystem first.
- 2
AI Engineering Roadmap
Map out your learning path stage by stage.
- 3
AI Engineer Skills
Audit which technical skills you need to build.
- 4
What is RAG?
Master the most deployed enterprise LLM pattern.
- 5
What Are AI Agents?
Understand how agents use tools, memory, and workflows.
- 6
What is MCP?
Learn the protocol connecting AI to tools and data.
- 7
AI Engineer Projects
Build a portfolio of deployed, evaluated projects.
- 8
AI Engineering Course
Get structured live training to build everything above.
Want guided learning instead of reading everything yourself?
Reading guides is a great start. Building production RAG pipelines, LangGraph agent workflows, and deployed AI services with live instruction, code review, and structured feedback is what gets you hired or gets your team shipping. Technovids offers live programmes for every path.