Learn how modern AI systems are actually designed in production — not just how demos work. Twelve weeks, live mentorship, and mandatory hands-on projects.
LANGCHAIN · LANGGRAPH · RAG · FASTAPI · DOCKER · VECTOR DBs · LANGSMITH · MCP · LANGFUSE
Welcome
"This program is designed for engineers who want strong AI fundamentals and the skills to take AI applications into production as applied AI practitioners. You won't just learn tools. You'll learn to design and debug real AI systems with confidence. Welcome to Learn With Sarvesh, where learning turns into real-world AI systems."— Sarveshwaran R, Founder, Learn With Sarvesh
Instructors
The gap
Many courses teach AI tools and step-by-step tutorials. Building real AI systems requires deeper understanding than that.
Core philosophy
"Tools change. Mental models last."
Who this is for
You should be comfortable with basic programming concepts, writing and reading code, and debugging errors.
This is a skills-first program.
Curriculum
Every phase builds on the last — from engineering hygiene to multi-agent systems talking to real tools. Open a week to see what you'll learn and build.
Most AI projects fail due to poor engineering hygiene.
You'll stop treating LLMs as magic.
Control the chain, control the outcome.
Agents amplify design decisions — good or bad.
Retrieval quality decides answer quality.
If you can't evaluate it, you can't trust it.
Production begins where tutorials end.
Optimization is what separates demos from products.
Hands-on: a mini LangGraph workflow — User Query → Retrieval → Answer Generation.
Hands-on: a Supervisor → Specialist → Writer agent workflow.
Hands-on: build a simple MCP server and connect it to a model.
Capstone: an MCP-powered AI agent that connects to external tools, retrieves real-world data, and automates tasks across systems.
Format
3 hours, live, every week — concept sessions for foundations, project-build sessions during project weeks, with live focus on debugging, design review, and Q&A.
Every alternate Saturday, dedicated to doubt-clearing or a guest lecture from a practicing AI engineer.
Structured, reusable, deep content you can revisit anytime.
Mandatory and production-oriented — not optional extras.
Reinforce every module so concepts stick beyond the live session.
Awarded for finishing the hands-on work — not just watching videos.
Included
Bonus inclusions
Everything you get when you enroll in the AI Engineer Ready Program — beyond the 12-week core curriculum.
Basics · 6 hrs · Beginner friendly. Master Python from scratch for AI, Data Science & projects.
8 hrs · Intermediate level. Build on your Python skills with advanced concepts, projects & real-world applications.
Live session · 24 hrs. Build real-world AI projects with mentorship from Sarvesh.
Recordings, slides, resources, and assignments from past cohorts.
Stack
This mirrors real industry usage — the same stack applied AI teams build with.
What you'll build
Real AI engineers are defined by the systems they build.
Career outcomes
Prompt engineering alone is not enough — real value comes from building AI systems.
Certificate
This certificate is right for you if you want to:
This certificate reflects effort, thinking, and engineering discipline — not just course completion. Its value comes from the work behind it.
Complete the cohort — including its mandatory hands-on projects — and earn an official Certificate of Completion from Learn With Sarvesh.
Proof
"Before this cohort, I thought AI was just prompting models. Now I design complete AI systems where memory, tools, and retrieval work together to build real-world applications."
"Earlier AI felt like magic. Now I understand how LLM systems work and I can build RAG pipelines and agent workflows with confidence."
"This cohort gave me the confidence to build AI systems independently. Building AI agent tools and multi-client server workflows was the most exciting part."
"The course provided practical insights into how AI can be applied in real-world scenarios. The AI Agents module was my favourite."
"This cohort shifted my focus from just models to the importance of retrieval and context. Multi-agent orchestration showed how AI systems collaborate to solve complex tasks."
"This cohort helped me clearly understand how modern AI systems work. The hands-on projects made it easier to learn how LLMs, RAG, and AI agents can be applied to real-world problems."
"The course provided a clear and practical understanding of building AI systems. Learning how to connect prompts, tools, and retrieval pipelines was extremely valuable."
"This cohort gave me strong exposure to agentic AI concepts and modern LLM frameworks. The structured modules and projects helped me build confidence in developing AI applications."
"This program helped me build real-world AI applications like resume analyzers, chatbots, and document summarizers using LangChain and RAG pipelines."
"This program provided both strong theoretical understanding and hands-on experience through real projects. Learning RAG systems and agent orchestration significantly improved my confidence."
"This cohort helped me transition from traditional programming into AI development. I now understand how to design intelligent AI agents that can reason, use tools, and solve complex problems."
"I learned how modern AI systems work using LLMs, RAG, LangChain, MCP, and LangGraph. The LangGraph module helped me understand how AI workflows plan, execute, and validate tasks."
"This program provided both a deep understanding of AI systems and hands-on experience through industrial-level projects. Learning about LangChain and building voice agents significantly improved my confidence in developing real-world AI solutions and helped me in my current work."
In their own words
Enrollment details
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Build real AI systems and develop the engineering mindset required to deploy production-ready AI applications.
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