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
Everything you need to know
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