We are more than just a training partner
Every element of our program is structured to maximise learning — small groups, practical exercises, step-by-step guidance, and real project work.
Live Interactive Sessions
Real-time explanations and Q&A.
Cohort-Based Learning
Small groups for personalised guidance.
Hands-on Real Projects
Build GenAI applications you can showcase.
Flexible Weekend Schedule
Fits the routine of working professionals.
Structured Learning Path
Modules designed to build skills step-by-step.
Beginner & Advanced Tracks
Choose a path that suits your experience.
Your Learning Path
Every strong AI career starts with a solid base. At Techtonic, your learning path moves from deep fundamentals to applied projects, so you always know why something works—not just how to copy it.
Core Understanding
Foundations of LLMs, prompting, and embeddings.
Tools & Stack
Python, APIs, LangChain, vector DBs, and integration basics.
Guided Projects
Build your first Q&A bots, summarizers, and assistants.
Applied Systems
RAG apps, agents, workflows, and evaluation.
Portfolio & Next Steps
Polish projects, publish code, and position your skills for roles.
Structured, Skill-Focused Curriculum
Module 1: Generative AI Fundamentals & Core Concepts
Module 2: Advanced Prompting & Model Fine-Tuning
Module 3: LLM Architecture & Deep Learning Foundations
Module 4: Specialization in NLP or Computer Vision
Module 5: Production Deployment & MLOps for AI Systems
Module 6: Career Preparation & Industry Placement
Module 1: Foundations of Generative AI & Prompt Engineering
Understanding Generative AI and its real-world impact. Learn the core technological differences between Traditional AI and Generative AI, and master Prompt Engineering principles with production-ready prompt patterns for real applications.
Session 1: Generative AI & the Art of Prompting
- Understanding Generative AI and its real-world impact
- Traditional AI vs Generative AI — core technological differences
- Introduction to Prompt Engineering principles
- Implementing production-ready prompt patterns for real applications
Session 2: Large Language Models & Embedding Essentials
- Definition and operation of Large Language Models (LLMs)
- Overview of the LLM landscape (OpenAI, LLaMA, Mistral)
- Managing tokens, context windows, and model limitations
- Comprehensive guide to embeddings: types, use cases, and deployment
Module 2: LLM APIs & Conversational AI
Explore the OpenAI model ecosystem and learn to build production-ready conversational chatbots. Understand open-source LLMs like LLaMA and Mistral, and make strategic decisions about model selection for various applications.
Session 1: OpenAI Models & Conversational Chatbot Development
- Detailed walkthrough of the OpenAI model ecosystem
- Chat completion architecture and API usage
- Understanding and managing System, User, and Assistant roles
- Building a production-ready conversational chatbot with proper context management
Session 2: LLaMA & Mistral – Open-Source LLMs in Action
- Exploring the open-source LLM ecosystem and its advantages
- Strategic comparison: OpenAI vs. Open-source models for various applications
- Analyzing cost efficiency, latency, and deployment trade-offs
Module 3: LLM Implementation & Text Intelligence
Learn to implement open-source LLMs locally and master text intelligence techniques including summarization, data extraction, and structured output generation from unstructured text.
Session 1: Implementing LLaMA & Mistral Locally
- Setting up local environments for open-source LLM inference
- Fine-tuning generation parameters (temperature, top-p) for optimal results
- Advanced prompt optimisation techniques for open models
Session 2: Text Summarisation & Intelligent Data Extraction
- Extracting structured JSON outputs from unstructured text (e.g., using Mistral)
- Applying Named Entity Recognition (NER) and attribute extraction methods
- Techniques for long-document summarisation (e.g., recursive methods)
- Practical case study: Automated invoice data extraction
Module 4: Retrieval-Augmented Generation (RAG) Foundations
Understand the rationale for RAG in enterprise AI systems and learn to build complete RAG-powered Q&A systems. Master chunking strategies, embedding creation, and vector database integration.
Session 1: RAG Concepts & System Architecture
- Rationale for RAG in enterprise AI systems and its necessity
- Deconstructing the end-to-end RAG pipeline
- Effective chunking strategies for high-accuracy retrieval
- Detailed process of embedding creation and retrieval workflow
Session 2: Building a RAG-Powered Q&A System
- Ingesting and preprocessing diverse data formats (e.g., PDF)
- Introduction to vector databases: Qdrant / Chroma integration
- Implementing semantic similarity search for relevant information retrieval
- Constructing a complete RAG-based Q&A application
Module 5: Advanced RAG & Conversational SQL
Master advanced RAG optimization techniques and learn to build conversational interfaces that interact with SQL databases. Implement hybrid search, metadata filtering, and secure SQL query generation.
Session 1: Advanced RAG Optimisation Techniques
- Implementing metadata-driven filtering for precise retrieval
- Integrating hybrid search (keyword + semantic) strategies
- Accuracy tuning methodologies and hallucination reduction techniques
Session 2: Chat with SQL Databases
- Core process of converting Natural Language → SQL queries
- Schema-aware query generation for increased reliability
- Protocols for secure and safe SQL execution
- Developing conversational analytics interfaces with PostgreSQL
Module 6: Agentic AI Fundamentals
Understand AI Agents and their core components. Learn the differences between Chatbots, RAG, and Agents, and build single-agent systems with tool-calling capabilities and multi-step task execution.
Session 1: Understanding Agentic AI
- Defining an AI Agent and its core components
- The role of Tools, Memory, and Planning in agent architecture
- Comparative analysis: Chatbots vs RAG vs Agents
Session 2: Building Single-Agent Systems
- Practical application of tool-calling agents
- Designing robust decision-making and reasoning workflows
- Implementing multi-step task execution chains
- Exploring real-world agent use cases and implementations
Module 7: Multi-Agent Systems & N8N Automation
Design multi-agent architectures with role-based agent design and learn to orchestrate complex workflows using the n8n automation platform for building LLM-powered automation pipelines.
Session 1: Designing Multi-Agent Architectures
- Principles of role-based agent design
- Implementing the Planner → Executor → Reviewer agent pattern
- Strategies for effective inter-agent communication
- Techniques for robust error handling and recovery in multi-agent systems
Session 2: Multi-Agent Workflows using n8n
- Introduction to the n8n automation platform
- Utilizing webhooks, triggers, and workflow nodes for automation
- Building LLM-powered automation pipelines
- Assembling and orchestrating multi-agent systems within n8n
Module 8: FastAPI for Gen AI Applications
Master FastAPI essentials for building scalable AI systems. Learn to expose RAG and Agent systems as production-ready APIs with proper authentication, rate-limiting, and async capabilities.
Session 1: FastAPI Essentials for AI Systems
- Review of REST API fundamentals and principles
- Designing clear request and response schemas
- Leveraging Async APIs for highly scalable AI services
- Integrating LLMs directly into FastAPI endpoints
Session 2: Exposing RAG & Agent Systems as APIs
- Developing robust Chatbot APIs
- Creating dedicated RAG and Agent endpoints
- Best practices for API authentication and rate-limiting
Module 9: Dockerisation & Cloud Deployment
Learn Docker fundamentals for containerizing Gen AI applications and deploy them to cloud environments. Master production-grade deployment considerations including monitoring and scaling.
Session 1: Docker for Gen AI Applications
- Docker fundamentals and containerization concepts
- Writing optimized Dockerfiles for FastAPI + LLMs
- Strategies for environment variables and secrets management
- Implementing container best practices for production
Session 2: Cloud Deployment on Virtual Machines
- Virtual machine setup and configuration
- Deploying Dockerised AI applications to cloud environments
- Production-grade deployment considerations (monitoring, scaling)
Module 10: Capstone Project
A comprehensive project integrating all skills learned throughout the program to build a robust, production-ready Generative AI solution. This capstone demonstrates your mastery of the complete GenAI development lifecycle.
Capstone: End-to-End Gen AI System
- Integrating conversational AI, RAG, and agentic capabilities
- Building a complete production-ready application
- Implementing proper API design and deployment
- Demonstrating real-world problem-solving with GenAI
- Portfolio-ready project showcasing all learned skills
Build Real, Portfolio-Ready Projects
Not toy demos—build projects that showcase real skills to employers and clients alike.
AI Study Assistant
Build a chatbot that answers questions from your own PDFs and notes.
Learn basic RAG concepts and document ingestion.Email & Document Summarizer
Create a simple app to summarize long emails, reports, or articles.
Focus on prompting, API usage, and clean UX.Task & Idea Brainstorm Bot
Build a personal assistant that helps you brainstorm ideas and to-do lists.
Practice conversational AI design and prompt engineering.Choose Your Learning Path
From fundamentals to enterprise-ready GenAI—built the right way.
Core GenAI Systems Builder
- Understand how LLMs, embeddings, and prompting work in real-world systems
- Build production-style GenAI applications (chatbots, RAG, summarization)
- Work with APIs, vector databases, and structured outputs
- Design conversational workflows using prompts and retrieval
- Deploy GenAI applications using FastAPI and Docker
- Weekly live sessions with hands-on guidance and doubt clearing
Advanced GenAI Systems & Architecture
- Design enterprise-grade GenAI architectures at scale
- Advanced RAG evaluation, optimization, and reliability techniques
- Agentic workflows with governance, guardrails, and cost control
- Security, observability, and performance tuning for GenAI systems
- Real-world system design based on Core Track learnings
About Techtonic
Techtonic is a specialized Generative AI learning institute for working professionals. We focus on deep understanding, hands-on practice, and real-world application—so you don't just follow tutorials, you learn how to build systems that matter.
Our Team
Techtonic is led by three senior professionals with over a decade of experience each across software engineering, data, and AI. They've built and shipped real products, and they bring that practical mindset into every session, project review, and discussion.
Contact
Location:
31, Poochiyur Rd, Hitech City Phase 1, Coimbatore,
Tamil Nadu 641031
Email:
techtoniclearning@gmail.com
Call:
+91 9342965762