https://github.com/mlane/llm-getting-started
Practical, beginner-friendly LLM projects using Python, LangChain, and LangSmith. Modular, reusable, and easy to run.
https://github.com/mlane/llm-getting-started
agent agents ai-examples ai-getting-started ai-projects beginner-friendly chatgpt gpt langchain langchain-examples llm llm-apps llm-examples ollama openai prompt-engineering python python-llm python3 text-generation
Last synced: 12 days ago
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Practical, beginner-friendly LLM projects using Python, LangChain, and LangSmith. Modular, reusable, and easy to run.
- Host: GitHub
- URL: https://github.com/mlane/llm-getting-started
- Owner: mlane
- License: mit
- Created: 2025-03-25T23:37:12.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-03-26T00:45:31.000Z (10 months ago)
- Last Synced: 2025-03-26T01:32:03.970Z (10 months ago)
- Topics: agent, agents, ai-examples, ai-getting-started, ai-projects, beginner-friendly, chatgpt, gpt, langchain, langchain-examples, llm, llm-apps, llm-examples, ollama, openai, prompt-engineering, python, python-llm, python3, text-generation
- Language: Python
- Homepage:
- Size: 2.93 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# LLM Getting Started
A growing collection of practical, beginner-friendly projects using **Python**, **LangChain**, and **LangSmith** to explore modern LLM patterns.
> Inspired by the [LLM Engineering Cheatsheet](https://github.com/mlane/llm-engineering-cheatsheet)
---
## Quick Start
```bash
git clone https://github.com/mlane/llm-getting-started.git
cd llm-getting-started
python3.11 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
cp .env.sample .env
# Fill in your OpenAI / LangSmith API keys
# Run an example project
python3.11 projects/debate_agent.py
# Set up pre-commit hooks
pre-commit install # (run once per machine)
```
---
## Python Standards
- **Python version**: 3.11+
- **Formatter**: [`black`](https://github.com/psf/black)
- **Linter**: [`ruff`](https://github.com/astral-sh/ruff)
```bash
# Format code
black .
# Lint code
ruff check .
```
---
## Project Roadmap & Learning Path
This repo will grow over time. Projects are grouped by complexity to help you build intuition as LLM concepts evolve from simple to advanced.
✅ = Implemented & ready to run
💡 = Planned or conceptual for now
| Level | Project | Concepts Practiced | Status |
| --------------- | ------------------------------- | -------------------------------------- | ------ |
| 🟢 Beginner | Simple ChatBot with Memory | Interactive session, short-term memory | ✅ |
| 🟢 Beginner | Zero/Few-Shot Prompt Playground | Prompt patterns, zero-shot thinking | 💡 |
| 🟡 Intermediate | LLM Agent Debate | System prompts, disagreement modeling | ✅ |
| 🟡 Intermediate | Role-Based Support Assistant | Formatting, role control | 💡 |
| 🔴 Advanced | Retrieval QA from Local Docs | Vectorstores, retrieval chain | 💡 |
| 🔴 Advanced | Multi-Agent Task Planner | LangGraph, agent chaining | 💡 |
---
## Concept Glossary
Each script lists one or more of the following **concepts** it demonstrates:
### LLM Behaviors (Descriptive)
| Behavior | Concept |
| ------------------------------------ | --------------------------------------- |
| No examples given | zero-shot reasoning |
| Examples in prompt | few-shot prompting |
| Explicit persona or tone | persona control, system prompts |
| Responds to previous turns | conversation history, short-term memory |
| Simulates disagreement or debate | agent disagreement |
| Builds on previous answers | turn-based dialogue |
| Explains steps | chain of thought |
| Uses external data for context | RAG (Retrieval Augmented Generation) |
| Uses retrieved data to augment model | document retrieval, model augmentation |
### Interaction Patterns
| Structure/Flow | Concept |
| ------------------------------------ | -------------------------------------------------- |
| Live user input loop | interactive session |
| Two or more agents taking turns | multi-agent interaction |
| Message-based prompt passing | conversational flow |
| Uses tools or actions | tool execution |
| Semantic search for relevant context | document retrieval, semantic search, vectorization |
Use these as a reference when reading or extending scripts.
---
## Philosophy
We believe the best way to learn LLMs is by **doing** — each script is small, focused, and teaches a core idea.
This repo is built to be:
- Modular
- Beginner-friendly
- Focused on **thinking**, not just syntax
- Updated as the LLM ecosystem evolves
---
## License
[MIT](./LICENSE)
PRs welcome. Please keep things clean, consistent, and low-dependency.