Generative AI Mastery
Build LLM products that actually work: RAG systems with citations, tool-using agents, guardrails, evaluations, and production deployment.
From Prompts to Reliable Products
The Generative AI program is built for builders: not "cool demos" - but systems that stay grounded, scale, and handle real user behavior.
You'll master RAG, build tool-using agents, design guardrails, and set up evaluation + observability so quality doesn't collapse in production.
Program Outcomes:
- ->Design prompts + structured outputs that stay consistent
- ->Build RAG pipelines with chunking, metadata, and citations
- ->Create agents that use tools safely and deterministically
- ->Add guardrails: policy, PII redaction, jailbreak resistance
- ->Evaluate quality and monitor regressions in production
GenAI Tool Stack
LangChain
LLM Orchestration
LlamaIndex
RAG Framework
Vector DB
Embeddings Store
Embeddings
Semantic Search
Agents
Tool Use / Planning
Guardrails
Safety & Policy
Eval Harness
Quality Metrics
Docker
Deployment
Structured Learning Path
LLM Fundamentals
Tokens, context windows, latency/cost, model behaviors & limitations.
Prompt Engineering
System prompts, few-shot, structured outputs, prompt versioning.
Embeddings & Retrieval
Embeddings, similarity search, hybrid retrieval, re-ranking basics.
RAG Architecture
RAG design patterns, query rewriting, citations, grounded answers.
Chunking & Indexing
Chunk strategies, metadata, filtering, ingestion pipelines.
Agents & Tools
Agent loops, tool calling, function schemas, memory patterns.
Fine-tuning & LoRA
Instruction tuning, LoRA concepts, datasets, when/why to tune.
Guardrails & Safety
Policy filters, PII handling, jailbreak resistance, safe outputs.
Evaluation & Observability
Offline evals, human feedback, tracing, drift & regressions.
Capstone: Production GenAI
Ship a full GenAI product: RAG + Agents + Guardrails + Monitoring.
FROM PROMPTS TO PRODUCTS.
We build creators of next-generation AI systems.
Wall of Fame
Frequently Asked Questions
No. We start with fundamentals and progressively move into Python, model building, and deployment workflows.
Yes. You build portfolio projects in data science, machine learning, and applied AI use-cases with mentor feedback.
You work with Python, notebooks, model libraries, data pipelines, and practical deployment practices used in production teams.
Typical outcomes include AI/ML Intern, Junior Data Scientist, Machine Learning Engineer (entry level), and AI Analyst roles.
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