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Python Virtual Environments

What is a virtual environment?

A virtual environment (venv) is an isolated Python environment that allows you to install packages for a specific project without affecting your system Python or other projects.

Virtual environments are particularly useful when different projects require different versions of the same library.

For example, one project can use LangChain 0.2 while another uses a newer version, with no conflicts between their dependencies.

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Practice: Resume Parser with Structured Outputs

Instructions

For this exercise, your task is to turn free-text resumes (like the one shown below) into a Python object using Structured Outputs.

Starter code:

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Practice: Prompt Engineering in Action

Setup

from openai import OpenAI
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())

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Tips & Recommendations for Project 1 (NLP Automated Customer Reviews)

Dataset

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Practice: for loop

Iteration 1

You have a list of AI models. Print each one on its own line.

models = ["GPT-4", "Claude", "Gemini", "Llama", "Mistral"]

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🔴 Red Team Challenge

Red teaming is the practice of deliberately trying to break, misuse, or bypass a system so its weaknesses can be found and fixed before they cause real problems.

In AI engineering, this means going beyond asking “Does the model work?” and asking:

• “How could someone misuse this?”
• “What assumptions is the developer making?”
• “What happens when the user doesn't follow the intended path?”
• “Can I make the model ignore its instructions?”

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NLP Evaluation Metrics

1. Intro to BLEU (machine translation)

Watch this video:

• "What is the BLEU metric?" (5 minutes): https://www.youtube.com/watch?v=M05L1DhFqcw

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Open Models vs. Closed Models

Open-weight models (e.g. Kimi3, Llama, Mistral, Phi)

Advantages

• Can fine-tune on your own data
• More privacy and control (can run on your own infrastructure)
• No per-token API costs (you pay for compute instead)
• Can run offline or in air-gapped environments
• Full control over model version and deployment

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LangChain Versions: Evolution, Terminology, and Key Differences

LangChain has evolved significantly since its initial release. Rather than thinking in terms of dozens of individual releases, it's more useful to understand its development as three major eras.

Era	Common name	Approx. timeframe	Main characteristics
Pre-0.1	Early LangChain	2023	Experimental framework centred around chains
0.1–0.3	LangChain v0 / LCEL LangChain	2024–2025	Introduction of LCEL and the Runnable architecture
1.x	LangChain v1	2025–present	Agent-first framework built on top of LangGraph

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Practice: LangChain Agents

Setup

Install dependencies:

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