Field notes on AI, agents and analytics.
Guides on Generative AI, LLMs, RAG and AI agents from the Skillancy blog, where I'm Head Instructor. Each article opens on skillancy.in.
This page collects 13 guides from the Skillancy blog on Generative AI, prompt engineering, RAG vs fine-tuning, AI agents and data careers, curated by Rishav Kumar, Head Instructor at Skillancy.
Generative AI & agents
LLMs, RAG, prompting and AI agentsAI Agents & Multi-Agent Orchestration: The Complete 2026 Guide
How single agents and teams of agents plan, use tools and run whole workflows, and how to design them safely.
Read article ↗ Generative AIAgentic AI for Data Analytics: How AI Agents Are Transforming SQL, BI & Reporting
Where AI agents help analysts today: text-to-SQL, anomaly investigation, automated reporting and data-quality checks, with guardrails.
Read article ↗ Generative AIRAG vs Fine-Tuning: What Should You Use for Generative AI Applications?
A side-by-side guide to choosing between retrieval and fine-tuning based on freshness, behaviour, citations, cost and upkeep.
Read article ↗ Generative AIGenerative AI Roadmap 2026: From LLM Fundamentals to RAG and AI Agents
A step-by-step learning path from LLM basics and prompting through embeddings, RAG, tools, agents, evaluation and deployment.
Read article ↗ Generative AIPrompt Engineering for LLMs: The Complete 2026 Guide for Data & AI Professionals
Structured, testable prompting techniques that turn inconsistent LLM output into something you can ship.
Read article ↗ Generative AIHow to Become an AI Engineer in India in 2026: Skills, Tools, Projects & Roadmap
The skills, tools and portfolio projects that matter for AI engineering roles in India, from Python to deployed agents.
Read article ↗Data science & analytics
Roadmaps, skills and careersData Analytics with AI in 2026: How AI Is Changing the Data Analyst Role
How text-to-SQL, automated reporting, feedback analysis and agentic workflows are reshaping day-to-day analytics work.
Read article ↗ Data ScienceData Science Roadmap 2026: Python, Statistics, Machine Learning, GenAI & Projects
A practical sequence for data science learners, ending with GenAI, deployment and portfolio projects.
Read article ↗ Data ScienceHow to Become a Data Scientist in India in 2026: Complete Roadmap
What to learn, build and prepare for interviews to land a data scientist role in India.
Read article ↗ Data AnalyticsData Analyst Skills in 2026: What You Actually Need to Learn to Get Hired
The analyst skills hiring managers look for now, from Excel, SQL and BI to Python and AI-assisted analysis.
Read article ↗ Data AnalyticsHow to Choose a Data Analytics Course in India for Working Professionals in 2026
A checklist for comparing courses on curriculum, projects, mentorship, schedule and career support.
Read article ↗ Data AnalyticsHow to Become a Data Analyst in India in 2026: Skills, Roadmap, Projects & Jobs
A roadmap from first spreadsheet to job offer, covering tools, projects and interview preparation.
Read article ↗ Data AnalyticsData Analytics: The Future Skill Every Professional Needs
Why organisations in every industry keep hiring for analytics, and what that means for your career.
Read article ↗Blog FAQ
Who writes these articles?
The articles are published by the Skillancy Editorial Team on skillancy.in and curated here by Rishav Kumar, Head Instructor at Skillancy.
What is the difference between RAG and fine-tuning?
RAG retrieves up-to-date information from your own documents at query time and gives it to the LLM, which makes it best for changing knowledge and answers with citations. Fine-tuning trains the model further on examples to change its behaviour, style or format. Many production systems combine both.
Where should a beginner start learning Generative AI?
Start with how LLMs work and prompt engineering, then learn embeddings and RAG, and then move to AI agents, evaluation and deployment. The Generative AI roadmap and prompt engineering guides on this page follow that order.
Prefer to learn this live?
Workshops, bootcamps and masterclasses on Generative AI, LLMs, RAG and AI agents.