TechLabsMind Training
Applied GenAI Roadmap
A comprehensive, project-driven learning path from AI foundations to production-grade Generative AI systems — covering LLMs, RAG, Agentic AI, fine-tuning, and enterprise deployment.
8Learning Phases
24+Hands-on Modules
18–24Weeks Duration
100%Project-Based
What You Will Achieve
Learning Outcomes
Build production-grade RAG systems and AI agents from scratch
Fine-tune open-source LLMs on custom domain data
Deploy and monitor AI applications on Azure, AWS, and GCP
Apply responsible AI practices and enterprise governance
Lead AI transformation initiatives within your organisation
Earn industry-recognised AI certifications
The Complete Path
8-Phase Learning Roadmap
Each phase builds on the last — from core Python and ML fundamentals all the way to enterprise-grade GenAI deployment and governance.
Phase 12–3 Weeks
AI & ML Foundations
Python for AI
- Python refresher — data structures, OOP, file I/O
- NumPy, Pandas, Matplotlib for data manipulation
- Virtual environments, pip, Jupyter notebooks
Machine Learning Essentials
- Supervised vs unsupervised vs reinforcement learning
- Regression, classification, clustering algorithms
- Model evaluation — accuracy, precision, recall, F1
- Scikit-learn hands-on labs
Deep Learning Basics
- Neural networks — perceptrons, activation functions
- Backpropagation and gradient descent
- CNNs for image tasks, RNNs for sequences
- PyTorch / TensorFlow fundamentals
Phase 23–4 Weeks
Generative AI & Large Language Models
Transformer Architecture
- Attention mechanism — self-attention, multi-head attention
- Encoder–decoder architecture deep dive
- BERT, GPT, T5 family overview
- Tokenisation, embeddings, positional encoding
Working with LLMs
- OpenAI GPT-4o, Claude, Gemini, Llama 3 APIs
- Chat completions, function calling, structured outputs
- Token budgeting, temperature, top-p sampling
- Streaming responses and async patterns
Prompt Engineering
- Zero-shot, few-shot, chain-of-thought prompting
- System prompts and persona design
- ReAct, Tree-of-Thought, Self-Consistency
- Prompt injection risks and mitigation
Phase 32–3 Weeks
RAG & Knowledge Systems
Vector Databases & Embeddings
- Semantic search vs keyword search
- Embedding models — OpenAI, Cohere, sentence-transformers
- Pinecone, Weaviate, Chroma, pgvector hands-on
- Chunking strategies and metadata filtering
RAG Pipeline Design
- Naive RAG → Advanced RAG → Modular RAG
- Hybrid retrieval — BM25 + dense vectors
- Re-ranking with cross-encoders
- Evaluation — RAGAS, faithfulness, answer relevancy
LlamaIndex & LangChain for RAG
- Document loaders, text splitters, index types
- Query engines and response synthesisers
- Conversational memory and chat history
- Multi-document and multi-modal RAG
Phase 43–4 Weeks
Agentic AI & Multi-Agent Systems
AI Agents Fundamentals
- Agent loop — perceive, reason, act, observe
- Tool use and function calling patterns
- Memory types — in-context, external, episodic
- Planning strategies — ReAct, Plan-and-Execute
Agentic Frameworks
- LangGraph — stateful multi-step agent graphs
- AutoGen — conversational multi-agent orchestration
- CrewAI — role-based agent crews
- OpenAI Assistants API with code interpreter & file search
Multi-Agent Architectures
- Supervisor, hierarchical, and swarm patterns
- Agent communication protocols
- Human-in-the-loop checkpoints
- Debugging and observability with LangSmith
Phase 52–3 Weeks
Fine-Tuning & Model Customisation
Fine-Tuning Techniques
- Full fine-tuning vs parameter-efficient fine-tuning (PEFT)
- LoRA and QLoRA — theory and implementation
- Instruction tuning and RLHF overview
- Dataset preparation — formatting, cleaning, deduplication
Open-Source Model Fine-Tuning
- Hugging Face Transformers & PEFT library
- Fine-tuning Llama 3, Mistral, Phi-3 on custom data
- Training on Google Colab / RunPod / Lambda Labs
- Merging adapters and pushing to Hugging Face Hub
Evaluation & Alignment
- Benchmark evaluation — MMLU, HellaSwag, TruthfulQA
- Custom domain evaluation harnesses
- DPO (Direct Preference Optimisation) basics
- Safety, bias detection, and red-teaming
Phase 63–4 Weeks
GenAI Application Development
Building GenAI Apps
- FastAPI backend with LLM integration
- Streaming responses to frontend (SSE / WebSockets)
- Conversation state management and session handling
- File upload, parsing, and multimodal inputs
LangChain & LangGraph in Production
- LCEL (LangChain Expression Language) pipelines
- Stateful workflows with LangGraph checkpointers
- Caching, retries, and fallback chains
- Tracing and evaluation with LangSmith
Capstone Projects
- Enterprise document Q&A system (RAG + agents)
- AI-powered code review assistant
- Multi-agent research and report generation pipeline
- Customer support bot with escalation logic
Phase 72–3 Weeks
MLOps & Production Deployment
Model Serving & Inference
- vLLM, Ollama, TGI for self-hosted inference
- OpenAI-compatible API servers
- Batching, quantisation (GGUF, AWQ, GPTQ)
- GPU vs CPU inference trade-offs
Cloud Deployment
- Azure OpenAI Service and AI Studio
- AWS Bedrock and SageMaker JumpStart
- GCP Vertex AI and Model Garden
- Containerising AI apps with Docker
Monitoring & Governance
- LLM observability — latency, cost, token usage
- Drift detection and model refresh strategies
- Responsible AI — fairness, explainability, privacy
- Enterprise AI governance frameworks
Phase 81–2 Weeks
Security, Ethics & Enterprise AI
AI Security
- Prompt injection, jailbreaking, and adversarial attacks
- Data poisoning and model extraction risks
- Guardrails — NeMo Guardrails, Llama Guard
- PII detection and data anonymisation
Responsible & Ethical AI
- EU AI Act and global regulatory landscape
- Bias auditing and fairness metrics
- Explainability — SHAP, LIME, attention visualisation
- Building an AI ethics review process
Enterprise AI Strategy
- AI readiness assessment and maturity model
- Build vs buy vs fine-tune decision framework
- ROI measurement for AI initiatives
- Change management and AI adoption playbook
Is This For You?
Who Should Enrol
Software Engineers
Developers who want to integrate GenAI capabilities into their products and become AI-native engineers.
Data Scientists & ML Engineers
Practitioners looking to upskill from classical ML to modern LLM-based systems and production deployment.
Tech Leads & Architects
Senior engineers who need to design, evaluate, and govern AI systems at an enterprise scale.
Product & Business Leaders
Professionals who want to understand GenAI deeply enough to drive AI strategy and transformation initiatives.
Ready to Become AI Native?
Join TechLabsMind's Applied GenAI programme and build real-world AI systems from day one.