Corporate AI agent training that makes your engineers production-ready in one weekend
A hands-on, end-to-end workshop on LLMs, embeddings, RAG, agents, evals and deployment, built around your team's real workflows. Your engineers leave having shipped a working agent, not just watched slides.
- 12+ years
- building software & AI systems
- $300K+
- earned on Upwork delivering AI projects
- Principal
- AI Architect in industry
- 52+ hours
- of free AI engineering lessons on YouTube
Who it's for
Built for teams that need to ship AI, not just talk about it
Software engineers
Backend and full-stack developers who will build and own AI features.
Data & ML teams
Data scientists and ML engineers moving from models to production agents.
Tech leads & architects
People who decide what to build, what to buy and how it should be governed.
What your team walks away with
- A working agent built on one of your real workflows
- An architecture your team can extend: RAG, tools, memory and guardrails
- An evaluation setup to measure quality before every release
- A clear cost model: what each request and each task will cost
- A security checklist covering prompt injection and data access
- Hands-on lab code your team keeps as a reference
Curriculum
12 modules, 68 topics: from foundations to a shipped agent
Everything we can cover, from LLM internals to production, security and team strategy. Each batch is tailored to your stack and domain: we pick and adapt modules together on the discovery call.
- Module 01
LLM foundations that matter in production
How models actually behave, so your team stops guessing.
- How LLMs actually work: the foundation of AI agents
- Tokens, vectors & embeddings: a self-attention deep dive
- The Transformer You Learned Isn't Quite What Runs a 1M-Token Model
- LLM Controls Lab: Temperature, Top-p, Context and Structured Outputs
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- Do Agents Really Need 1 Million Tokens?
- How VLMs work: from pixels to AI agents
- Module 02
Prompt & context engineering, and cost
Reliable output at a predictable price.
- Prompt engineering skills every AI agent engineer needs (theory + hands-on)
- 23+ context engineering & agent memory techniques
- Caching Lab: Prompt Cache vs Semantic Cache
- 15+ techniques to reduce token cost in agents
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- Cheaper Per Token, Costlier Per Task
- Stop Using an LLM for Every Decision
- Module 03
RAG on enterprise data
Retrieval that holds up on real, messy, permissioned documents.
- The complete RAG architecture: embeddings, vector search & Pinecone
- How vector databases actually work
- 20+ chunking techniques for production RAG
- Hybrid Search Lab: BM25, Reranking and Query Rewriting
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- One Vector Per Document Isn't Enough: Late Interaction Explained
- GraphRAG Lab: When Is a Knowledge Graph Worth It?
- RAG Lab: Permissions and Fresh Data
- Build Private RAG That Runs Offline, With No API Bill
- You Have 1 Million Tokens. Do You Still Need RAG?
- I Taught You 20 RAG Techniques. Stacking Them Can Backfire.
- Plain RAG Just Beat 12 Agent Memory Systems
- Module 04
Agent architecture, MCP & patterns
Designing agents that do real work, not demos.
- The complete agent architecture: tool calling, ReAct, memory, evals & traces
- 10+ enterprise agent patterns explained
- What is an agent harness?
- Why Your Agent Should Write Code Instead of Calling Tools
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- Skills, MCP or AGENTS.md: What Goes Where?
- How MCP works, from zero to production
- MCP Just Deleted Its Own Handshake
- WebMCP & loop engineering
- Human Approval Lab: Let Agents Ask Before They Act
- Computer-Use Agents: Cheap Enough Now. Safe Enough?
- Build a Voice Agent That Handles Interruptions
- Module 05
Hands-on build: LangChain, LangGraph & Langfuse
Your team builds an agent step by step, the way it's done at work.
- Build an AI agent from scratch with LangChain + LangGraph
- Tracing and debugging with Langfuse
- Memory, context & multi-agent with DynamoDB
- Guardrails, multi-agent & context engineering
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- Connecting agents to tools with MCP
- Module 06
Evals, observability & LLMOps
Ship prompt and model changes with confidence.
- How LLM evals work: Ragas, Langfuse & DeepEval
- Stop Shipping Prompt Changes Blind: LLMOps and CI/CD
- What Happens When Your Agent Fails at 3 AM?
- Kill Your Agent Mid-Run. Does It Survive?
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- 60 enterprise AI bottlenecks: LLMs, RAG & agents
- Module 07
Security & guardrails
Design for attacks before they happen.
- Your Agent Will Get Prompt Injected. Design for It.
- Where Should Agent-Written Code Run?
- Why Your Agent Shouldn't Call Model APIs Directly
- How an AI Gateway's Logs Became a Hacker's Way In
- Module 08
Production deployment & cost
From notebook to an API your business can rely on.
- Put Your Agent on AWS: Bedrock, AgentCore and the Real Bill
- From Notebook to Production API: How to Serve an Agent
- Should You Self-Host Open Models? The Real Bill
- Fine-Tune or Not? When Prompting and RAG Hit a Ceiling
- Module 09
AI coding agents for engineering teams
Get real productivity from coding agents without losing skills.
- Four Agent Harnesses, One Task: Which Should You Use?
- Claude Code, Codex, Cursor or Antigravity? One Real Feature Test
- How to Use Coding Agents Without Losing Your Skills
- OpenAI Had to Rebuild Git for Agents
- Module 10
Industry case studies & AI strategy
What the companies winning with agents actually did.
- How Coding Agents Became the Biggest Agent Businesses
- What Sierra, Harvey and Legora Teach About Vertical Agents
- Why Is a Workflow Engine Worth $12.55 Billion?
- Why a Bank Moved 2,000 Engineers to Open Models
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- The forward deployed engineer (FDE) role and why it matters
- Module 11
Building & hiring an AI engineering team
For leads and managers growing AI capability in-house.
- What 390 AI Engineer Job Posts Actually Ask For
- FDE Jobs in India: What Changed in 2026?
- AI-Allowed Coding Interviews: How to Pass Them
- The Portfolio That Proves You Can Ship AI Agents
- Module 12
Capstone on your use case
Your team builds and ships one production agent.
- Capstone: Build and Ship One Production AI Agent
- Pick a real workflow from your business
- Evaluate, harden and plan the deployment
Your trainer
Abhishek Gupta
Principal AI Architect · Founder, Nitmonk
12+ years building software and AI systems, with $300K+ earned delivering AI projects for clients on Upwork. Abhishek works as a Principal AI Architect and teaches AI engineering to thousands of developers through the free course on nitmonk.com and the @nitmonk YouTube channel. Your team learns what actually holds up in production, from someone who builds it.
Learners are already landing AI roles with this material
![WhatsApp message: Hey bro, just wanted to genuinely thank you for all the AI content you've shared on YouTube. There are so many AI playlists out there, but what I really liked about yours is that you teach everything in a practical, real-world way, rather than just theory. It feels like you're actually sharing things based on how you've worked on them in real projects, and that made a huge difference for me. I completed your playlist, and it helped me crack multiple interviews, get 2–3 offers, and reach the final stage of 4–5 companies. Finally, I've decided to join [company hidden] as a Senior AI Engineer. Your content genuinely played a big role in this journey. Really appreciate all the effort you put into teaching. Once again, thanks a lot](/_next/image?url=%2Ftestimonials%2Fwhatsapp-senior-ai-engineer.webp&w=3840&q=75)
How it works
From first call to a shipped agent
- 1
Discovery call
30 minutes to understand your team, stack and the workflows worth automating.
- 2
Tailored agenda
You get the full module plan, adapted to your domain, data and cloud.
- 3
Weekend workshop
Two intensive days of teaching and hands-on labs, ending with a capstone build.
- 4
Follow-through
Reference code and next steps so the team keeps shipping after Monday.
Questions companies ask
What does the ₹99,999 include?
One 2-day weekend workshop (sat–sun) for up to 25 participants: discovery call, tailored agenda, teaching, hands-on labs and the capstone build. GST is extra.
Can the workshop be customised to our industry?
Yes. Every batch is built around your domain and data: the labs and the capstone use workflows your team actually has, whether that's finance, healthcare, retail, logistics or SaaS.
Do participants need prior AI experience?
No prior AI experience is needed, but participants should be comfortable writing code (Python preferred). We share free pre-reading from the Nitmonk course before the workshop.
Is it on-site or online?
Workshops currently run live online, which suits distributed teams and keeps costs down. On-site sessions at your office can be discussed on a case-by-case basis.
How do you handle our confidential data?
Labs can run on sample or anonymised data, or inside your own cloud account. We're happy to sign an NDA before the discovery call.
Get started
Book a discovery call
Tell us about your team and what you want to build. We'll reply with available weekends and a tailored agenda.
Prefer to chat? Use the chat button to message Abhishek directly.