AI Assistants & Copilot Engineering

This instructor-led course teaches developers how to build reliable AI assistants and copilots and how to use LLMs effectively as collaborative development tools in team environments. Moving beyond basic prompt engineering, the course focuses on assistant architecture, model behavior control, tool integration, memory, retrieval, evaluation, and AI pair-programming skills used in modern workflows with tools such as Claude Code.

Participants build a production-ready Enterprise AI Copilot while learning transferable skills for using LLMs to support design, implementation, testing, and documentation—establishing a strong foundation for multi-agent systems explored in the advanced course.

Learning Objectives

Assistants & Copilot Engineering

• Design system prompts and interaction contracts for assistant-style applications
• Control model behavior using schemas, constraints, and refusal patterns
• Integrate tools and APIs into assistants using structured tool calling
• Implement memory and retrieval strategies for multi-turn assistants
• Build retrieval-augmented assistants grounded in domain knowledge
• Evaluate assistant quality using functional checks and LLM-as-judge scoring

AI Pair Programming & Team Copilot Skills

• Use LLMs like Claude Code as effective development partners
• Decompose problems and guide AI reasoning through iterative prompting
• Manage context, scope, and task boundaries when working with LLMs
• Use AI for design exploration, refactoring, testing, and code review
• Establish team practices for safe, effective AI-assisted development

Core Concepts Covered

This course explicitly teaches the following AI Copilot & Assistant Engineering skills:

• AI pair programming workflows and prompt patterns
• Context management and scope control
• AI-assisted design and architectural reasoning
• Structured outputs and guardrails
• Tool-augmented assistants
• Memory and retrieval strategies
• Model selection and configuration
• Evaluation and regression testing
• Team norms and risk management for AI usage

Course Modules

Day 1: Assistants & AI as a Development Partner

Lab 1: Assistant Architecture & Prompt Design - Students design a task-focused AI assistant, defining roles, responsibilities, and interaction boundaries to produce predictable, accountable behavior.

Lab 2: AI Pair Programming with Claude Code–Style Workflows - Students use LLMs as development partners to:

• Explore problem spaces
• Generate and compare design options
• Reason about tradeoffs
• Document decisions

Lab 3: Structured Outputs & Guardrails - Students implement JSON schemas, validation, and refusal behavior, learning how structure improves both assistant reliability and AI-assisted development.

Day 2: Tools, Memory, Retrieval, and Models

Lab 4: Tool-Enabled Assistants - Students integrate backend APIs into assistants and design decision logic for when to invoke tools, mirroring real copilot use cases.

Lab 5: Memory & Context Management - Students implement conversation memory, summarization, and context windows, while applying the same techniques to manage long-running AI-assisted development tasks.

Lab 6: Retrieval-Augmented Assistants - Students build a RAG-powered assistant grounded in domain knowledge and explore how retrieval improves both user-facing assistants and developer copilots.

Day 3: Quality, Teams, and Production Readiness

Lab 7: Model Engineering & Evaluation - Students experiment with model selection and configuration, implement functional evaluators, and use LLM-as-judge techniques to assess quality and detect regressions.

Lab 8: AI in a Team Development Environment - Students establish practical team workflows, including:

• AI-assisted code review
• Test generation and refactoring
• Prompt reuse and versioning
• Documentation support
• Discussion includes failure modes, over-reliance, and governance considerations.

Technology Stack

Component	Technology	Purpose
Language	Python 3.11+	Core development
LLMs	Claude, GPT-class models	Assistants & development
AI Dev Tools	Claude Code / IDE copilots	Pair programming workflows
Tool Calling	OpenAI / Anthropic APIs	Assistant integration
RAG	FAISS + sentence-transformers	Knowledge grounding
Evaluation	Custom + LLM-as-judge	Quality assurance

 

Capstone Project

The Enterprise AI Copilot combines structured prompting, tool integration, memory, and retrieval into a single assistant designed for real workflows. Alongside the copilot, participants define AI-assisted development practices that demonstrate how LLMs can safely and effectively support engineering teams from design through delivery.

• Developers comfortable scripting and building automation in Python (or similar languages)

• Familiarity with REST APIs

• Basic experience using LLMs