AI Tools & Libraries Guide
A practical, project-organized guide to the generative AI and LLM tooling ecosystem. Learn what each major library or SDK does, when to use it, how to install it, and see a working example — across 24 lessons grouped by category, covering LLM provider SDKs, orchestration frameworks, vector databases, RAG, fine-tuning, and more.
Start Learning →What is This Course?
Building with AI today rarely means training a model from scratch — it means calling an LLM provider's API, wiring in a framework to orchestrate prompts and tools, storing embeddings in a vector database, and stitching all of it into a working application. That stack moves fast, and a new library or SDK seems to show up every month.
This course is a project-organized field guide to that stack. Instead of teaching machine learning theory from scratch, it catalogs the tools you'll actually reach for when building with generative AI — grouped by category — with the use case, the exact install command, the minimal setup, and a working code example for each one.
Most "AI engineering" work today is really software engineering around a model, not model-building itself — which is why fluency with the surrounding tools (orchestration frameworks, vector databases, evaluation tooling) matters just as much as understanding the model itself.
Where is This Used?
This knowledge applies directly any time you are building an application on top of AI models:
Chatbots & Assistants
Wire an LLM provider SDK into a real conversational product with memory and tools.
Retrieval-Augmented Generation
Ground model answers in your own documents using embeddings and a vector database.
Autonomous Agents
Chain multiple tool calls and reasoning steps together with an orchestration framework.
Running Models Locally
Recognize when a local runner (Ollama, llama.cpp) fits better than a hosted API.
Technical Interviews
Explain what a tool does and why you'd reach for it — an increasingly common interview topic.
Shipping AI Features to Production
Know which tools handle evaluation, cost monitoring, and observability once you're live.
Real-World Examples
Here are some practical scenarios this course prepares you for:
Example 1: A Support Chatbot
Use an LLM provider SDK plus function/tool calling to answer questions and trigger real actions.
Example 2: "Chat With Your Docs"
Embed a set of PDFs into a vector database and build a RAG pipeline to answer questions grounded in them.
Example 3: A Research Agent
Use LangChain or CrewAI to chain search, summarization, and reasoning steps into one autonomous flow.
Example 4: An Offline AI Feature
Run an open-weight model locally with Ollama so a feature works without a hosted API dependency.
Why Learn This?
Career
- AI tooling fluency is now a common requirement across frontend, backend, and data roles alike
- A very common interview and take-home-project topic in 2026
- Signals real hands-on building experience, not just prompting a chatbot
- Directly useful for evaluating which tool a team should actually adopt
Practical Skill
- Turns "I can call an API" into "I know exactly what to reach for and why"
- Saves hours of comparing frameworks and reading changelogs per task
- Builds a mental map of the entire generative AI tooling landscape at once
- Makes reading any unfamiliar AI project's dependencies far less intimidating
Broad Coverage
- 24 lessons across provider SDKs, orchestration, vector databases, RAG, and more
- Every entry has a working example — not just a name and a description
- Organized by category so you can jump straight to what you need
- Covers both the Python and JavaScript/TypeScript sides of the ecosystem
Ecosystem Fluency
- Understand how LLM provider SDKs, orchestration frameworks, and vector databases fit together
- See how local inference tools complement hosted APIs rather than replace them
- Learn how to keep API keys and secrets out of source control
- A natural companion to our Data Science Dependencies course
Code Example
Here is the shape of what every lesson covers — a tool, its install command, and a minimal working example. This one calls an LLM provider's chat API:
pip install openaifrom openai import OpenAI
client = OpenAI() # reads OPENAI_API_KEY from the environment
response = client.chat.completions.create( model="gpt-4o-mini", messages=[ {"role": "user", "content": "Summarize this course in one sentence."} ],)
print(response.choices[0].message.content)Click Run to see what this code prints.
One SDK (openai) plus a handful of expressive calls (client, messages, choices) is enough to go from an idea to a working AI feature — this is the pattern every lesson in this course follows for a different tool.
Course Curriculum
Follow these 24 lessons sequentially — they move from setup foundations through each major category of the AI tooling stack to a final real-world wrap-up.
Start with the foundations (environment setup, choosing a tool) before jumping into any category — those first four lessons explain *why* the ecosystem is organized the way it is, which makes every later lesson click faster. The category lessons can then be read in order or referenced individually as you need them.
Frequently Asked Questions
Yes — this course assumes you already know the basics of at least one of Python or JavaScript. Most lessons show Python, with lesson 20 dedicated to the JS/TS side of the ecosystem.
No. This course is a dedicated, categorized reference to the generative AI tooling ecosystem — SDKs, frameworks, and infrastructure each with a use case, setup, and example — rather than a deep dive into the math behind how models work.
Data Science Dependencies covers the classical ML/data-science stack (pandas, scikit-learn, PyTorch, TensorFlow in depth). This course focuses on the generative AI/LLM application layer built on top of that — provider SDKs, orchestration, vector databases, RAG, and agent frameworks. Lessons 7 and 8 briefly bridge the two.
Most provider lessons use paid APIs (OpenAI, Anthropic, etc.), though most also offer free trial credit. Lesson 18 covers fully local, free alternatives (Ollama, llama.cpp) if you'd rather avoid API costs entirely.
Our Data Science Dependencies course pairs well if you want to go deeper into the ML fundamentals underneath these tools, and Python is a good prerequisite if you haven't taken it yet.
Key Takeaways & Summary
- Building with generative AI today is mostly software engineering around a model, using a fast-moving stack of SDKs and frameworks.
- LLM provider SDKs (OpenAI, Anthropic, and others) are the foundation — everything else orchestrates or extends what they can do.
- Vector databases and embeddings are what let a model answer questions grounded in your own data, via RAG.
- This 24-lesson course groups 20+ real tools by category — provider SDKs, orchestration, vector databases, fine-tuning, local inference, and more — each with a use case, setup, and example.
- The goal is fluency: knowing which tool solves which problem, and how to wire it in confidently.
Begin with the Introduction lesson to understand how the generative AI tooling landscape fits together, then move through the categories — or jump straight to the category you need right now. Every lesson's example is meant to be copied into a real project and run.