Production AI Engineering: RAG, Agents & MCP
India's most advanced corporate AI engineering programme. Build production-grade RAG systems, autonomous LangGraph agents, and Model Context Protocol integrations — with a live expert, in your codebase, using your tech stack.
- or 8-week cohort
- 5 Daysor 8-week cohort
- Portfolio-ready builds
- 5 ProjectsPortfolio-ready builds
- LangChain to MCP
- 15+ ToolsLangChain to MCP
- Python required
- AdvancedPython required
Your developers have access to LangChain. But are they building production systems?
Most AI engineers in India today are building brittle RAG pipelines from tutorials — systems that work in a Colab notebook and break in production. The gap between "I followed the LangChain quickstart" and "I shipped a reliable RAG system serving 500 internal users" is wider than most teams realise.
of enterprise RAG deployments fail retrieval quality benchmarks in production
of structured instruction to go from tutorial-follower to production AI engineer
is the skill fewer than 1% of Indian AI engineers have — your team can be first movers
The Complete Production AI Stack
Every tool your team needs to build, evaluate, and deploy AI systems in production. No toy examples — the same frameworks used by AI engineering teams at Swiggy, Razorpay, and Indian unicorns.
Pinecone
Managed vector DB — production default for most teams
Weaviate
Open-source, hybrid search + GraphQL API
ChromaDB
Local-first, ideal for rapid prototyping
FAISS
Meta's library for billion-scale ANN search
LangChain
Industry standard for LLM pipelines and RAG chains
LlamaIndex
Best-in-class for document ingestion and index management
LangGraph
Stateful agent workflows — production-grade agents
Semantic Kernel
Microsoft SDK — native Azure and Teams integration
CrewAI
Role-based multi-agent collaboration framework
AutoGen
Microsoft's conversational multi-agent framework
LangGraph
Graph-based stateful agent execution
ReAct Framework
Reason + Act paradigm — the foundational agent pattern
MCP (Anthropic)
Model Context Protocol — emerging enterprise AI standard
OpenAI Function Calling
Structured tool use with GPT-4o
Gmail / Slack / Calendar
Real workflow automations via APIs
PostgreSQL / SQLite
Database-querying agents with natural language
Five Modules. Five Deployable Projects.
Every module ends with a working project your team commits to their GitHub. No slides-only theory — every concept is demonstrated live and immediately applied.
RAG Foundations — From Theory to First Deployment
- →RAG architecture: retriever, generator, and the retrieval-augmented context window
- →Vector embeddings — how text becomes numbers, and why similarity search works
- →Chunking strategies: fixed-size, recursive, semantic, and document-aware chunking
- →Vector database setup: Pinecone, ChromaDB, and FAISS — trade-offs and selection criteria
- →Similarity search, hybrid search (BM25 + dense), and when to use each
- →Re-ranking with cross-encoders: improving retrieval precision before generation
- →Metadata filtering: scoped retrieval for multi-tenant and access-controlled systems
Participants deploy a working RAG system from scratch by end of Day 1.
Advanced RAG — Production Patterns That Actually Work
- →Why naive RAG fails in production: the 7 most common retrieval failure modes
- →Multi-query RAG: generating multiple search angles from one user question
- →Query rewriting and HyDE (Hypothetical Document Embedding) for sparse queries
- →Context compression: reducing noise before the LLM sees the context window
- →Graph RAG: knowledge graph extraction for relationship-aware retrieval
- →Agentic RAG: dynamic retrieval decisions instead of fixed pipeline steps
- →RAG evaluation frameworks: RAGAS, TruLens — measuring retrieval quality scientifically
- →Hallucination detection and guardrails for production safety
Teams measure and improve retrieval quality with quantified metrics.
Agentic AI — Building Autonomous Systems with LangGraph & CrewAI
- →AI agent fundamentals: perception, planning, memory, and action loops
- →ReAct framework: Reason + Act — the pattern behind every modern agent
- →LangGraph deep dive: nodes, edges, conditional routing, and state management
- →Single vs multi-agent: when one agent is enough and when to orchestrate a crew
- →CrewAI: defining agent roles, goals, and task delegation in natural language
- →AutoGen: conversational multi-agent frameworks for collaborative problem solving
- →Memory systems: conversation memory, entity memory, and external memory stores
- →Reflection loops and self-correction: agents that fix their own mistakes
Participants build agents that complete multi-step tasks without human intervention.
Tool Calling, Automations & Production Integration
- →Function calling / tool use: OpenAI, Anthropic, and Gemini — API differences and patterns
- →Designing tool schemas for reliable structured output from LLMs
- →Gmail, Slack, and Calendar agents: real workflow automation with OAuth
- →Database-querying agents: natural language → SQL → result → narrative
- →Web browsing agents: Playwright + LLM for autonomous web research
- →Memory persistence: short-term, long-term, and episodic memory architectures
- →Error handling and retry logic for production agent reliability
- →Observability: LangSmith and Arize for tracing, debugging, and monitoring agent runs
Teams ship agents connected to real tools that solve actual workflow problems.
MCP (Model Context Protocol) — The Emerging Enterprise Standard
- →What is MCP? Architecture, ecosystem, and why it matters for enterprise AI
- →MCP vs REST APIs: the key differences in how AI clients discover and call tools
- →MCP clients and servers: how Claude Desktop, Cursor, and custom apps use MCP
- →Building a custom MCP server from scratch: tool exposure, schema design, security
- →Connecting internal databases to MCP: safe read/write access patterns
- →Integrating third-party APIs as MCP tools: Jira, Confluence, Salesforce, SAP
- →Claude MCP integrations: computer use, filesystem, and custom toolsets
- →AI interoperability: architecting multi-model systems using MCP as the glue layer
Participants leave with a complete, deployable AI system using the latest enterprise standard.
5 Projects Your Team Ships & Owns
Not demos. Not replicas of tutorials. Systems your engineers designed, built, and debugged — ready to deploy or extend in your product.
PDF Chatbot
LangChain · ChromaDB · OpenAI
Upload any PDF. Ask natural language questions. Get cited, page-referenced answers. The foundational RAG project — teaches chunking, embedding, similarity search, and answer generation end-to-end.
Resume Search Engine
LlamaIndex · Pinecone · Hybrid Search
Semantic search over hundreds of resumes. Filter by skills, experience, and location using metadata filtering + hybrid BM25 search. Real HR tool — teaches production vector database management.
Company Knowledge Assistant
LangChain · RAGAS · Advanced RAG
Multi-document RAG system with evaluation dashboard showing retrieval quality scores. Includes hallucination detection, context compression, and a web UI. The most deployable enterprise project.
YouTube Transcript Chatbot
Multi-query RAG · LangGraph · Context Compression
Process YouTube transcripts, auto-chunk by speaker and topic, answer questions about video content with timestamp references. Teaches agentic retrieval decisions and advanced context management.
Multi-Agent Workflow System + MCP Server
LangGraph · CrewAI · MCP · Tool Calling
The capstone. A team of AI agents — researcher, writer, reviewer — completing a full workflow, orchestrated by LangGraph, with a custom MCP server exposing your internal tools. Production-deployable.
All code on your GitHub
Every project is committed to your team's repository during the training. You own the code. It is yours to deploy, extend, and show in interviews.
Who Should Attend
This is a technical course. Intermediate Python is a hard prerequisite — not negotiable.
Python Developers (3–8 yrs)
Have the coding foundation. Need the LLM systems layer. Will immediately apply RAG and agent skills to their current product work.
Perfect fitML / Data Scientists
Know the theory. Lack production system skills. The LangGraph + production deployment modules close this gap.
Perfect fitBackend Engineers assigned to AI projects
Suddenly responsible for RAG pipelines with no LLM training. This is the fastest path to competence.
Perfect fitSenior Engineers & Tech Leads
Need to architect AI systems, evaluate frameworks, and review team output. Module 5 (MCP) is especially relevant.
Strong fitDevOps / Platform Engineers
Building AI infrastructure and observability tooling. Module 4 (production deployment, LangSmith) is the core value.
Good fitNon-developers / Business Users
This course requires Python coding. Business-focused AI training is available separately.
Not this courseTwo Delivery Formats
Same curriculum. Same outcomes. Choose the format that fits your team's schedule.
Duration: 40 hours over 5 consecutive days
Delivery: At your office, any city in India
Schedule: Mon–Fri · 9 AM – 6 PM IST
Best for: Teams that want rapid transformation without splitting focus
- ✓Dedicated lab environment setup at your office
- ✓All equipment provided by Technovids
- ✓Daily project reviews and code walkthroughs
- ✓On-call trainer access during lab hours
Duration: 40 hours over 8 weeks (2 × 2.5 hr sessions/week)
Delivery: Live instructor-led, Zoom or Teams
Schedule: Weekday evenings or weekend mornings · flexible
Best for: Teams with ongoing project commitments who need spaced practice
- ✓Recorded session replays within 24 hours
- ✓Async Q&A between sessions via Slack
- ✓Weekly project code review
- ✓Peer cohort of 8–12 developers from other companies
What Your Team Walks Away With
Production-deployable portfolio pieces
LangChain, LangGraph, Pinecone, CrewAI, MCP & more
Hands-on live instruction + project builds
Project-based — no slides-only sessions
Post-training WhatsApp support + code review
Technovids AI Engineering certification
What Engineering Teams Say
“Our team had been using LangChain for months but building fragile pipelines we were afraid to deploy. The Advanced RAG module showed us exactly what we were doing wrong. Retrieval precision went from 61% to 88% in two days of refactoring.”
Vikram Rao
Lead AI Engineer · Bengaluru-based SaaS product company
“The LangGraph section was the most valuable thing I've done professionally in two years. We were using basic chains for everything — now our agents handle complex multi-step workflows without breaking. The code quality of our AI team improved overnight.”
Sneha Joshi
ML Engineering Manager · Mumbai-based fintech
“We came for RAG and stayed for MCP. Nobody else in India is teaching MCP at this depth. We shipped our first internal MCP server in the week after the training — it changed how our entire team thinks about AI integrations.”
Arjun Kapoor
Staff Engineer · Hyderabad IT services firm
Investment & What's Included
- ✓40 hours of live, hands-on instruction (5-day or 8-week format)
- ✓All 5 portfolio projects — your code, your GitHub
- ✓Pre-training technical discovery call to customise to your stack
- ✓RAG evaluation dashboard template (RAGAS integration ready)
- ✓LangGraph agent architecture reference guide
- ✓MCP server starter kit (custom-built for your team)
- ✓Technovids AI Engineering certificates for all participants
- ✓30-day post-training WhatsApp support + code review channel
- ✓Access to updated course materials for 6 months
💡 Also available: 1:1 mentorship format for individual engineers. 3 months, personalised pace. View mentorship programme →
Corporate Pricing
₹ On Request
Per team · Based on batch size & format
Typically ₹35,000–₹60,000 per participant
depending on format and team size
Teams that deploy even one of the five projects internally typically recover the training cost within a single sprint. We will show you the ROI calculation on our discovery call.
Request a Proposal →WhatsApp Us NowFrequently Asked Questions
What is RAG and why does every enterprise AI team need it?+
RAG (Retrieval-Augmented Generation) is the architecture that connects LLMs to your company's own data — documents, databases, knowledge bases. It's the most deployed AI pattern in enterprise production because it solves the hallucination problem for domain-specific queries while keeping responses current without expensive model retraining.
What Python level do participants need?+
Intermediate Python — comfortable with functions, classes, dictionaries, and making API calls. Experience with async Python is helpful. We do not cover Python basics. If your team can build a simple REST API in Python, they are ready.
How is LangGraph different from LangChain?+
LangChain is for pipelines — linear chains of prompts, retrievers, and tools. LangGraph adds stateful, cyclic graph execution — agents that loop, branch, reflect, and maintain state across many steps. LangGraph is the production standard for autonomous agents in 2026.
What is MCP and why does it matter?+
MCP (Model Context Protocol) is Anthropic's open standard for AI-to-tool communication. It lets you build reusable tool integrations that any MCP-compatible AI client can discover and call. It's the emerging enterprise interoperability standard — being an early mover matters.
Can this be customised for our existing tech stack?+
Yes. We run pre-training discovery calls and adapt projects to your cloud platform (AWS/Azure/GCP) and existing infrastructure. AWS teams build with Bedrock + pgvector; Azure teams use Azure AI Search; GCP teams use Vertex AI — same curriculum, your stack.
5-day intensive or 8-week cohort — which is better?+
The 5-day intensive is faster and creates stronger team cohesion. The 8-week format allows spaced practice, which leads to deeper retention and better production-quality code. For teams with ongoing commitments, 8 weeks is more realistic. Both cover identical curriculum.
What is the ideal batch size?+
Eight to sixteen participants. Small enough for the trainer to debug code live for each team; large enough for diverse problem-solving. For larger teams, we run parallel batches.
How is this different from online courses?+
Online courses give you tutorials to replicate. This programme gives you a live expert who debugs your specific code, adapts to your company's use case, and ensures you build working systems — not just follow along. The difference shows up in your GitHub, not your certificate.
Ready to build your team into AI engineering leaders?
Tell us about your team — technology stack, experience level, and goal. We will send a customised programme proposal and pricing within 24 hours.