Learning Path
GenAI Foundations
Core GenAI concepts, APIs, prompts, structured outputs, RAG, agents, evals, and production AI basics.
What is Generative AI and How It Works
Understand what generative AI actually does - not the hype, but the mechanism. How text, images, and code come out of a model and why it matters for your role.
Understanding Large Language Models (LLMs)
Tokens, context windows, temperature, and why hallucinations happen - the core mechanics every practitioner needs to know before building with AI.
How to Use APIs to Access AI Models
Make your first AI API call. Understand the difference between OpenAI, Anthropic, and Google APIs, and learn the request/response pattern that powers every AI application.
Writing Effective Prompts
The 4-part anatomy of a great prompt: Role, Task, Context, Format. Learn zero-shot, one-shot, and few-shot techniques that actually work in production.
Structured Input vs Structured Output
Why unstructured AI responses break your application and how to use JSON mode to get predictable, parseable output every time.
Generating Clean Structured Data Using Schemas
Use Pydantic and JSON Schema to constrain AI output to exactly the shape your code expects. No more parsing failures or unexpected fields.
Prompt Templates and Dynamic Prompts
Hard-coded prompts don't scale. Learn how to build reusable, testable prompt templates with variable substitution - from f-strings to LangChain PromptTemplate.
How LangChain Connects Everything Together
LangChain's LCEL syntax lets you chain prompt → model → parser in a single expression. Build your first AI pipeline in 10 lines.
How Real-World AI Applications Are Structured
The 4-layer architecture of production AI apps: UI, API, AI engine, and data. Where failures happen at each layer and how to design for resilience.
Build Your First RAG System
Retrieval-Augmented Generation: give your AI access to your documents. Build a working RAG pipeline with ChromaDB and OpenAI in under 50 lines.
Building AI Agents: From Zero to First Autonomous Task
Agents use tools, make decisions, and loop until they solve a problem. Build a tool-using agent from scratch and understand the ReAct pattern that makes it work.
Tool Use and Function Calling
The function calling protocol lets LLMs request structured tool execution. Master the request/response cycle, parallel calls, and error handling patterns.
Evaluating Your AI Application
Build an eval suite that actually catches problems. LLM-as-judge, assertion-based testing, and the eval pipeline that should gate every deployment.
Context Window Management
Context windows are finite and expensive. Learn the truncation strategies, context budgeting, and chunking patterns that keep your AI app fast and affordable.
Memory Patterns for Conversational AI
Stateless LLMs need explicit memory management. Buffer memory, summary memory, and entity memory - when to use each and how to implement them.
Multi-Model Strategies: Routing, Fallbacks, and Cost Tiers
Not every task needs GPT-4. Route simple queries to cheap models, complex ones to powerful models, and build fallback chains that survive model outages.
AI Testing Strategies for QA Engineers
The QA playbook for non-deterministic systems. Snapshot evals, property-based testing, regression suites, and the test pyramid adapted for AI applications.
Production RAG Architectures and Self-Healing Patterns
Move beyond basic RAG to production-grade retrieval: hybrid search, self-query, re-ranking, and self-healing loops that detect and repair retrieval failures.
Multi-Agent Systems and Orchestration Patterns
Supervisor, parallel, and sequential multi-agent patterns. Design systems where specialized agents collaborate, with state management and failure handling.
AI System Observability and Monitoring
What to log, how to trace, and how to detect drift before users do. Build the observability stack that turns AI black boxes into diagnosable systems.
Security: Prompt Injection, PII, and Red Teaming Your AI App
Prompt injection attacks, indirect injection via documents, PII leakage through context, and how to red team your AI application before attackers do.
Fine-tuning vs RAG vs Prompting: A Decision Framework
When to prompt-engineer, when to RAG, and when to fine-tune. A decision framework with cost, complexity, and quality trade-offs mapped out.
Writing AI Specifications for Engineers
The BA/PM guide to writing AI feature specs that engineers can actually implement. Eval criteria as acceptance criteria, prompt requirements, and edge case handling.
AI Cost Optimization at Scale
Token costs, prompt caching, batching, model routing, and response caching. Techniques that turn a $50K/month AI bill into $12K without sacrificing quality.
Deploying AI Systems: CI/CD, Eval Gates, and Rollbacks
AI deployments need eval gates, not just unit tests. Build the CI/CD pipeline that validates AI quality before every deploy and rolls back on degradation.
Enterprise MCP and Tool Architecture
Move from ad-hoc function calls to protocolized, auditable tool integrations using MCP and enterprise connector patterns.
Agent Runtime Durability: Checkpoints, Resume, and Human Approval
Build agent workflows that survive crashes, pauses, and human approvals without corrupting state or duplicating side effects.
Context and Memory Engineering for Enterprise Agents
Design memory layers and context budgets that improve quality and lower cost in long-running enterprise workflows.
Agent Evaluation Harness: Trace Grading and Release Gates
Build workflow-aware evals that grade not just final answers, but the trajectory of tool use and decisions.
AI Governance: Guardrails, Prompt-Leak Defense, and Oversight
Implement governance controls that prevent data leaks, unsafe actions, and silent policy violations in agentic systems.
Agent Interoperability and A2A Patterns
Design multi-agent systems with clear contracts so teams can mix runtimes and frameworks without brittle rewrites.
Long-Running Agents and Async Operations
Build background agent workflows with polling, cancellation, retries, and user-visible progress for enterprise reliability.