AI Engineering
Build, ship, and operate AI-powered products. Prompt engineering, RAG, LLM gateways, observability, security, synthetic data, model compression, and production deployment - the full stack.
12 Modules. API to Production.
From prompt engineering to AI security - every layer of shipping real AI products, explained with depth.
LLMOps
Operationalize LLM-powered systems - prompt versioning, fine-tuning pipelines, CI/CD, and evaluation-driven development.
What you'll master
- What is LLMOps?
- Prompt Versioning
- Fine-Tuning Pipelines
- LLM CI/CD
- Dataset Curation for Fine-Tuning
- Evaluation-Driven Development
- LLMOps Platforms
7 lessons
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AI Observability
Instrument every LLM call - tracing, quality metrics, feedback collection, and alerting on degradation.
What you'll master
- Tracing LLM Applications
- LangSmith Deep Dive
- Langfuse
- Phoenix by Arize
- OpenTelemetry for AI
- Quality Metrics in Production
- Feedback Collection
- Alerting on Quality Degradation
8 lessons
Start for Free →
LLM Gateways
Route, cache, load-balance, and cost-manage LLM traffic with production-grade gateways.
What you'll master
- Why an LLM Gateway?
- LiteLLM
- PortKey
- Semantic Caching
- Model Fallback & Retry
- Load Balancing Across Providers
- Cost Management & Budget Alerts
- Rate Limiting & Quotas
8 lessons
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Synthetic Data
Generate, filter, and validate synthetic training data with LLMs - Self-Instruct, Evol-Instruct, and distillation.
What you'll master
- Why Synthetic Data?
- LLM as Data Generator
- Self-Instruct
- Evol-Instruct
- Distillation Datasets
- Data Quality & Filtering
- Synthetic Data for RAG
- Privacy & Ethics
8 lessons
Start for Free →
Model Compression
Run large models efficiently - quantization (GPTQ, AWQ), pruning, distillation, and LoRA.
What you'll master
- Why Compression?
- Quantization Deep Dive
- GPTQ
- AWQ
- Knowledge Distillation
- Structured Pruning
- Unstructured Pruning
- LoRA for Efficient Fine-Tuning
- Benchmarking Compressed Models
9 lessons
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AI Security
Defend AI systems against prompt injection, jailbreaks, data poisoning, and adversarial attacks.
What you'll master
- Prompt Injection
- Jailbreaks & Bypasses
- Data Poisoning
- Model Extraction
- Membership Inference
- Adversarial Examples
- Red Teaming AI Systems
- Securing RAG Systems
- AI Security Governance
9 lessons
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Production AI Patterns
Context management, streaming, async calls, batching, idempotency, and multi-tenant AI architecture.
What you'll master
- Context Management at Scale
- Streaming Responses
- Async LLM Calls
- Batch Processing with LLMs
- Idempotency & Retries
- Cost Optimization Patterns
- Multi-Tenant AI Systems
- AI Product Architecture
8 lessons
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AI Product Engineering
Build the product layer - streaming UX, latency budgets, error handling, feature flags, and product quality metrics.
What you'll master
- AI Product Design Principles
- Streaming UX for LLMs
- Handling LLM Latency
- AI Error Handling & Fallbacks
- Prompt UX Patterns
- AI Feature Flags & Rollouts
- Personalisation & Memory
- Measuring AI Product Quality
8 lessons
Start for Free →
Human-in-the-Loop
Annotation pipelines, active learning, escalation patterns, and feedback loops that improve AI systems over time.
What you'll master
- Why HITL Matters
- Annotation Pipelines
- Active Learning
- Human Feedback Collection
- Escalation & Handoff Patterns
- Review Queues & Tooling
- Measuring HITL Effectiveness
7 lessons
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Prompt Engineering
Design prompts that are reliable, testable, and production-grade - not just clever one-liners.
What you'll master
- Prompt Design Fundamentals
- System Prompts & Personas
- Few-Shot & Chain-of-Thought
- Prompt Templates & Composition
- Prompt Versioning & Management
- Prompt Injection Defense
- Prompt Debugging Methodology
- Advanced Prompting Techniques
8 lessons
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RAG Engineering
Build RAG systems that work in production - ingestion, chunking, retrieval, reranking, HyDE, and evaluation.
What you'll master
- Why RAG Exists
- Document Ingestion & Chunking
- Embedding Models in Production
- Vector Search in Practice
- Hybrid Search & Reranking
- Query Transformation & HyDE
- Agentic RAG
- RAG Evaluation: RAGAS
8 lessons
Start for Free →
AI Evaluation
Build eval pipelines that give real signal before and after deployment - LLM judges, golden datasets, and CI/CD evals.
What you'll master
- Why AI Evaluation is Hard
- Offline vs Online Evaluation
- LLM-as-Judge
- Building Golden Datasets
- RAG-Specific Evaluation
- Regression Testing for Prompts
- Continuous Eval in CI/CD
7 lessons
Start for Free →
Ready to ship AI products that hold up in production?
From prompt engineering to multi-tenant AI architecture - the complete AI Engineering curriculum.
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