Software Engineers
You know Python and APIs, and you want to integrate LLMs into real products — not just call OpenAI endpoints, but build robust, cost-effective AI features.
GenAI Engineering Training (Architect-Led)
Move beyond basic ChatGPT prompts to building production-grade GenAI applications. Learn to architect RAG pipelines, fine-tune models, build AI agents, implement guardrails, and integrate LLMs into existing systems — all with a focus on cost optimization, reliability, and responsible AI practices that production systems demand.
The gap between a working demo and a production GenAI system is enormous. Demos do not handle hallucinations at scale, do not track token costs that spiral to thousands per day, and do not deal with users who find creative ways to break your guardrails. This training bridges that gap. You will learn to design retrieval pipelines that actually return relevant context, build agent workflows that fail gracefully when tools return unexpected results, and implement evaluation frameworks that catch quality regressions before your users do. Every concept is grounded in the engineering reality of shipping AI features that work reliably, cost-effectively, and responsibly.
Who this training is for
Engineers building production AI systems.
Backend & Full Stack Developers
You are tasked with adding GenAI capabilities to existing applications and need to understand embeddings, vector stores, retrieval strategies, and production deployment.
Data Engineers & ML Engineers
You have ML experience and want to extend your skills into LLM-based systems, RAG architectures, and the emerging patterns for AI agent orchestration.
Tech Leads Making AI Decisions
You need to evaluate build-vs-buy for AI features, choose between models and providers, and design architectures that will not lock you into a single vendor.
What you will learn
From fundamentals to production-grade AI systems.
LLM Fundamentals & Model Selection
Understand transformer architectures at a practical level. Compare OpenAI, Anthropic, open-source models, and learn to choose the right model for your use case based on quality, latency, cost, and data privacy requirements.
Prompt Engineering & Chain-of-Thought
Go beyond basic prompting. Design structured prompts, implement few-shot learning, build chain-of-thought reasoning, and create prompt templates that produce consistent, reliable outputs across edge cases.
RAG Pipeline Architecture
Build end-to-end retrieval-augmented generation systems. Choose embedding models, configure vector stores like Pinecone and Weaviate, implement chunking strategies, and design retrieval pipelines that return genuinely relevant context.
AI Agents & Tool Use Patterns
Design agent architectures that plan, execute, and self-correct. Implement tool calling, multi-step reasoning, memory management, and the error handling patterns that prevent agents from going off the rails.
Fine-Tuning & Model Customization
Learn when fine-tuning makes sense versus RAG or prompt engineering. Prepare training datasets, run fine-tuning jobs, evaluate results, and understand the cost-benefit analysis for customized models.
Guardrails & Responsible AI
Implement input validation, output filtering, toxicity detection, and hallucination mitigation. Design systems that handle adversarial inputs and fail safely when the model produces unreliable results.
Cost Optimization for LLM Applications
Track and control token usage, implement caching strategies for repeated queries, choose model tiers strategically, and design architectures that balance quality with cost at scale.
Production Deployment & Monitoring
Deploy GenAI applications with proper observability. Monitor latency, track quality metrics, implement A/B testing for prompts, and build evaluation pipelines that catch regressions before users notice.
Real production projects
Build AI systems that work beyond the demo.
RAG-Powered Knowledge Base with Semantic Search
Build a production knowledge base that ingests documents, generates embeddings, stores them in a vector database, and retrieves contextually relevant answers. Implement hybrid search, re-ranking, and evaluation metrics to measure retrieval quality.
OpenAI API
LangChain
Pinecone
FastAPI
Python
AI Agent for Automated Code Review
Create an AI agent that analyzes pull requests, identifies code quality issues, suggests improvements, and explains its reasoning. Implement tool use for repository access, multi-step analysis, and structured output for actionable feedback.
AI Agents
Tool Calling
GitHub API
Structured Output
Guardrails
Multi-Model Orchestration Pipeline
Design a pipeline that routes requests to different models based on complexity, cost, and latency requirements. Implement fallback chains, quality scoring, and cost tracking across OpenAI, Anthropic, and open-source models.
Model Router
LiteLLM
Redis Caching
Monitoring
Docker
Training format
Structured for real skill building.
Weekly 1:1 / Batch Sessions
60-90 minute live sessions combining concept explanation with hands-on implementation. Build working systems during sessions, not just talk about them.
Hands-On Assignments
Implement real GenAI features between sessions. Build RAG pipelines, experiment with prompt strategies, and deploy working applications with proper evaluation.
Architecture Reviews
Review your AI system designs for retrieval quality, cost efficiency, reliability, and responsible AI practices before investing in implementation.
Ongoing Support
Async guidance between sessions. Share your experiments, discuss model comparisons, debug retrieval issues, and get feedback on prompt designs.
Your instructor
Learn from a practicing architect.
Vijaykumar NJ
Software Architect • 20+ Years Experience
- Hands-on experience integrating LLMs and GenAI capabilities into production enterprise systems
- Architected RAG pipelines, AI agent workflows, and multi-model orchestration for real-world applications
- Deep understanding of cost optimization, reliability, and responsible AI practices at scale
- Experience across Fortune 500 companies and leading technology organizations
- Active practitioner staying current with the rapidly evolving GenAI landscape
Explore more
Related training & services
Get started
Start Your GenAI Engineering Training
Share your background and goals. We will respond with a tailored learning plan within 24 hours.