Course overview
What your team will get out of this course
Automate documentation updates with agents
Use AI for coding suggestions with open source LLMs
Master agent-led code reviews and quality checks
Gain advanced debugging skills with agents
Generate usable code by instructing agents
How the course is structured
- The course has 5 sprints, each two weeks long.
- In every sprint, there's live instruction on Zoom and a guided project.
- You'll build a fully-functional agent in each sprint.
A week in the life
Designed for busy professionals, our course includes two live 1-hour classes per week with an optional office hours every week.
Weekly Schedule
- 2 Live Classes: 1-1.5 hrs each
- Office Hours: 1-1.5 hours (optional)
- Outside Class: 2-3 hours on projects (recommended)
This course includes
Interactive live lessons online
Guided feedback and reflection
5 AI projects to apply learnings
Direct access to an instructor
Private community of peers
Certificate upon completion
What your team will learn
Fundamentals of LLMs
OpenAI ◦ Mistral ◦ Anthropic
- System vs. user prompts: Differentiating LLM inputs.
- Choosing the right LLM: Guide to selecting suitable LLMs.
- Prompt engineering: Strategies for effective prompt design.
Fundamentals of AI-Powered Apps
LangChain ◦ Pinecone
- Fundamental chaining: Basic chaining techniques and strategies.
- Vector embeddings: Enhancing AI with vector embeddings.
- RAG: Combining retrieval with generative models.
Fine-tuning
Ollama ◦ OpenAI
- Fine-tuning vs. RAG: Techniques for AI model adjustment.
- Open source LLMs: Exploring open-source LLM benefits.
- Fine-tuning open source LLMs: Training LLMs for specific tasks.
Performance and Maintenance
LangSmith
- Building performative AI apps: Creating task-specific AI apps.
- Connecting to dev workflows: Integrating AI with dev workflows.
- Advanced chaining: Complex chaining techniques and strategies.
Designing and Building Agents
LangChain
- Designing Agents: Key strategies for agent design.
- Creating and using
tools: Developing tools for agent objectives. ReActframework: Agent goal achievement.
Training and Deploying Agents
AWS
- Deploying agents: Strategies for real-world agent deployment.
- Communication, Monitoring, and Iteration: Enhancing agent performance.
- Training agents: Moderating automated workflows.
Multi-Agent Systems
LangGraph ◦ CrewAI
- Multi-Agent Systems: Solving problems with multi-agent systems.
- Agent decision making: Decision making with LangGraph.
- Performance and scalability: Considerations for future iteration.
Build agents in every project
What are AI agents
- Intelligent software programs designed to perform tasks autonomously.
- They make decisions and execute actions with minimal intervention.
- Agents continually adapt and learn to optimize their performance.
Tech Writer
Coder
QA Tester
Architect
Who is this course for
Software engineers and developers
Engineering leaders and managers
Data scientists and ML engineers
Why build agents over using generic tools
There's an AI gold rush unfolding, and it's essential for every business to develop custom solutions to stay ahead.
Where you are
- Generating code with GitHub copilot
- Creating simple OpenAI API wrappers
- Processing documents with ChatGPT
- Building prompt libraries
What we'll teach you
- Developing code review agents
- Automating technical doc updates
- Implementing accurate code generation
- Building multi-agent systems
Unlike generic off-the-shelf tools, custom AI solutions are tailored to your unique needs, giving you full ownership.
Custom AI apps are
✅ Tailored to your specific needs
✅ Made to seamlessly integrate in your ecosystem
✅ Designed to give you a competitive advantage
✅ Built with scalability and flexibility in mind
✅ Cost-effective compared to generic solutions
✅ Meant to give you full ownership and control
Any Questions?
