01 / 07
Neural Networks
The Foundation
Layers of simple decisions that learn patterns from data.
Think of it like a series of filters. Each layer looks for different features - edges, shapes, then whole objects. By stacking many layers, the network can learn incredibly complex patterns.
- Inspired by brain neurons
- Each layer learns different features
- "Deep" means many layers
02 / 07
RAG
Retrieval-Augmented Generation
AI that looks up information before answering.
Instead of relying only on what it memorized during training, the AI first searches a knowledge base for relevant information. It's like the difference between a closed-book and open-book exam.
- Query → Search → Retrieve → Generate
- Reduces hallucinations
- Can access up-to-date information
03 / 07
Agents
AI That Takes Action
AI that can use tools and complete multi-step tasks.
Instead of just answering questions, an agent can actually do things: search the web, run code, book appointments. It follows a loop: think about what to do, do it, observe the result, repeat.
- Think → Act → Observe → Repeat
- Can use external tools
- Completes multi-step goals
04 / 07
Prompt Engineering
The Art of Asking
How you ask matters as much as what you ask.
A vague question gets vague answers. A clear, structured prompt with examples gets precise results. Small changes in wording can dramatically change the output.
- Be specific and give examples
- "Role prompting" - tell AI who to be
- Structure helps (lists, steps)
05 / 07
Fine-Tuning
Specialized Training
Teaching the AI your specific style or knowledge.
The base model knows a lot about everything. Fine-tuning trains it further on your specific data, making it an expert in your domain. The knowledge becomes permanent.
- Like teaching a chef your recipes
- Expensive but powerful
- Permanent vs temporary (prompts)
06 / 07
Chain of Thought
Showing the Work
Making AI reason step by step.
For complex problems, asking the AI to "think step by step" dramatically improves accuracy. By breaking problems into smaller pieces, errors become easier to catch.
- "Let's think step by step"
- Breaks problems into pieces
- Shows reasoning, catches errors
07 / 07
Tool Use
Extended Capabilities
AI that can use calculators, search, and run code.
Language models aren't calculators - they can make math mistakes. By giving them access to tools, they can offload tasks they're not good at to specialized systems.
- AI alone can't do math reliably
- Tools extend capabilities
- Search, calculate, code, browse
Ready to explore more?
Try our interactive demos to see these concepts in action.