Python, Coding Copilot & LLMs

Python, Coding Copilot & LLMs

Build real-world coding fluency, harness AI copilots, and deploy intelligent agents

- in 3 days

In an era where code and AI converge, this three-day course empowers participants to build a strong foundation in programming with Python, then rapidly leap into working with AI tools that significantly amplify productivity: from the popular copilots to full-blown large language model (LLM) integrations, agents and the emerging protocol space.

The instructor brings over 30 years of industry experience and will steer the curriculum away from purely academic abstractions toward practical, in-demand capabilities: writing working code, leveraging AI for coding, integrating LLMs with existing systems (via frameworks such as LangChain, RAG, Model Context Protocol (MCP) and more), and building agents that act and reason. The goal is fun, hands-on, effective: enabling you to walk away with usable skills and working prototypes.

Learning Outcomes

By the end of the course, participants will be able to:

• Understand and write basic to intermediate Python code: variables, control structures, functions, modules, packages.
• Use an AI-powered coding assistant (e.g., Copilot or equivalent) productively: autocomplete, suggestions, code generation, debugging.
• Integrate an LLM or copilot-style tool into a code environment and workflow.
• Build retrieval-augmented systems (RAG) and use the LangChain framework to connect code + knowledge base + LLM.
• Understand and apply the Model Context Protocol (MCP) to connect agents or tools to LLMs in a scalable way.
• Design and implement simple agents: LLM-based systems that choose tools or actions, reason about tasks, call code or external services.
• Combine all of the above into a small working project: Python code + AI coding assistance + LLM integration + agent logic + tool connectivity.
• Gain confidence to explore AI‐augmented software engineering practices in their own work.

Prerequisites

To get the most out of this workshop, participants should have:

• Basic familiarity with programming concepts (variables, functions) though not necessarily in Python.
• A laptop with internet access and the ability to install software / Python packages.
• Interest in AI/ML, coding productivity, and next-gen tooling (no deep ML research background required).
• Willingness to experiment, ask questions, and engage in hands-on tasks rather than pure lecture.

Training Outline

Below is a detailed, nested outline of the topics and sub-topics we will cover across the three days — designed to build sequentially, layer by layer:

Introduction & Setup

• Course overview, objectives, how we’ll work.
• Environment setup: installing Python, VS Code (or preferred IDE), setting up Git, creating a virtual environment, installing required libraries.
• Introduction to AI coding assistants: what is “Copilot”-style assistance? Overview of LLMs, code generation, suggestions.

Part 1: Python from the Ground Up

• Basics of Python: syntax, variables, data types (strings, numbers, lists, dicts, sets, tuples).
• Control flow: if statements, loops (for, while), comprehension syntax.
• Functions: defining functions, parameters, return values, default and keyword arguments.
• Modules and packages: importing modules, using standard library modules (e.g., os, sys, json), installing third-party packages with pip.
• Data structures and working with them: list methods, dictionary methods, set operations; iteration patterns.
• File I/O and basic exceptions: reading and writing text and JSON files, handling errors with try/except.
• Basic object-oriented programming: classes, attributes and methods; simple inheritance.
• Working on short mini-projects: for example, a small command-line tool, or simple data processing script.

Part 2: Using Copilot / LLM for Coding

• Introduction to AI-assisted coding: what is a “copilot”, how it works, benefits and caveats.
• Setting up a coding assistant plugin: integrating Copilot (or similar) with your IDE, enabling suggestions, configuring settings for Python.
• Workflow exploration: using the assistant for code completion, generating functions, refactoring, writing docstrings, converting pseudo-code to code.
• Best practices: prompt engineering for code generation, how to review and validate generated code, avoiding over-reliance.
• Hands-on exercises: pair coding tasks where participants generate code with assistance, then review and refine it.
• Debugging and testing: using the assistant to help write unit tests, detect errors, and refactor existing code for clarity.

Part 3: Integrating LLMs with Code (LangChain, RAG, MCP etc.)

• What is retrieval-augmented generation (RAG)? Why it matters when working with LLMs and external knowledge.
• Introduction to the LangChain framework: chains, LLMs, prompts, agents, tools.
• Hands-on: building a simple LangChain application: load documents, create embeddings, query vector store, feed into LLM.
• Introduction to the Model Context Protocol (MCP): what it is, why it matters for building agents and connecting tools.
• Integrating MCP with LangChain: using langchain-mcp-adapters to treat MCP tools as LangChain tools.
• Hands-on exercise: create a small MCP server (or use an existing one) and connect it to your application, then query via LangChain.
• Designing toolchains: how to think about external tools, data sources, prompts, context.
• Discussion of architecture: chaining LLM calls, managing context, pipelines vs agents.

Part 4: Agents

• Definition: what is an “agent” in LLM/dev-context? A system that uses an LLM to decide actions, pick tools, and orchestrate workflows.
• Types of agent architectures: routers, reflection agents, tool-calling agents, multi-agent systems.
• Building an agent using LangChain: example tutorial of a search-tool agent.
• Exercise: design an agent that, given a user request, chooses between tool(s), calls them via MCP or LangChain, collects results, and responds.
• Challenges and best practices: managing context, looping reasoning, preventing runaway behavior, monitoring tool usage.
• Extending agents: multi-step tasks, orchestration, memory, asynchronous invocation.

Part 5: Putting it All Together - Capstone Project

• Project brief: Participants form small teams (or individually) to design and build a mini project combining the elements: Python code base + use of a coding assistant + LLM integration (via LangChain) + tool/knowledge integration (MCP/RAG) + an agent workflow.
• Project planning: define scope, choose tool(s) or domain (e.g., document Q&A, code documentation assistant, data summariser, interactive chatbot).
• Implementation: develop core Python system, integrate coding assistant into workflow, connect LLM + knowledge base + tools, build agent logic.
• Review & refine: peer-review sessions, instructor feedback, testing.
• Presentation: each team presents their project, demos functionality, discusses what worked and what could be improved.

Wrap-Up & Next Steps

• Recap of key takeaways.
• Discussion of how to continue from here: resources, communities, frameworks, advanced topics.
• What to explore next: production deployment, scaling agents, security/privacy, advanced LLM architectures.
• Q&A and feedback.

Notes on Pace & Style

• The course is designed to be fun but effective: each day mixes short lectures, demos, interactive exercises, and peer collaboration rather than long monologues.
• Rather than overloading with theory, emphasis is placed on doing: writing code, using tools, building real things.
• The instructor’s 30+ years of industry experience means participants will hear practical anecdotes, common pitfalls, industrial best practices, not just textbook examples.
• We’ll provide plenty of templates, code skeletons, and “live-coding” sessions so that participants of varying experience levels can engage meaningfully.