📖Lessons
Introduction to LLM Development Tools
Navigate the LLM development ecosystem and choose the right tools for your project
LangChain Fundamentals
Master LangChain for building powerful LLM applications with chains, prompts, and memory
Building with LangGraph
Create stateful agent workflows with graph-based execution and advanced control flow
RAG with LlamaIndex
Build powerful retrieval-augmented generation systems with LlamaIndex
Multi-Agent Systems with CrewAI
Build collaborative AI agent teams with roles, goals, and coordinated tasks
OpenAI Agents SDK
Build production agents with OpenAI's modern Agents SDK framework
Google Agent Development Kit (ADK)
Build enterprise agents with Google's code-first Agent Development Kit
Evaluation with RAGAS
Automated RAG evaluation with specialized metrics for retrieval and generation quality
Experiment Tracking with MLflow
Track, version, and manage LLM experiments systematically
Emerging Frameworks
Explore AutoGen, AgentCore, and other emerging LLM frameworks
Workshop: Building a Production System
Build complete production RAG + Agent system integrating multiple frameworks
Tool Selection & Best Practices
Decision frameworks and best practices for LLM development
🎯Missions
M-079Build a CrewAI-Style Agent Team
Nebula Corp wants to automate their content pipeline using a team of specialized AI agents, similar to CrewAI. Build a crew system where each agent has a role, goal, and backstory. Agents work on tasks sequentially, passing context between them. Implement the crew runner that assigns tasks to agents, collects outputs, and produces a final combined result.
M-077Build a LangChain-Style Processing Chain
Nebula Corp wants to adopt a chain-based architecture for their AI pipelines. Build a simplified LangChain-style chain system where each step transforms the input and passes it to the next. Implement a chain builder that supports sequential steps, conditional branching, and error handling — the core patterns used in LangChain's LCEL.
M-080Build a LangGraph-Style State Machine
Nebula Corp wants to model their customer support workflow as a graph-based state machine, similar to LangGraph. Build a workflow engine where nodes are processing functions, edges define transitions (including conditional edges), and state flows through the graph. The engine should detect cycles, support conditional routing, and track execution history.
M-081Build a RAGAS-Style RAG Evaluator
Nebula Corp needs to evaluate their RAG pipeline's quality using metrics inspired by the RAGAS framework. Build an evaluator that computes faithfulness (is the answer grounded in context?), answer relevancy (does it address the question?), and context precision (is the retrieved context relevant?). Produce a comprehensive evaluation report with per-question and aggregate scores.
M-078Build Your First Processing Chain
Nebula Corp wants to build composable AI pipelines where each step transforms the data and passes it to the next. Build a simple chain system inspired by LangChain: create individual processing steps, chain them together, and run data through the pipeline. Each step is a function that takes input and returns output.