Understanding the Machine
Before mastering prompts, you must understand the engine. Generative AI is not magic—it's math, probability, and architecture.
How AI Models Are Trained (and Why Distillation Matters)
Understand pre-training, fine-tuning, RLHF, and the shortcut that changed the market conversation.
Pre-training
- The model learns patterns from large-scale text, code, and other data.
- This is the expensive, compute-heavy foundation stage.
- It builds general capability, but not task-specific behavior.
Like reading a massive library to learn language patterns before doing any specific job.
Why this matters (economics + bottlenecks)
Training and retraining frontier systems is capital-intensive and capacity-constrained.
Fine-tuning and RLHF require quality data curation, evaluation, and reviewer cycles.
Distillation flow (core idea)
Tradeoff: distillation can improve cost and speed, but the student may not match the teacher on edge cases or peak capability.
Read the full distillation explainer + case study
Use the blog post for dated claims, sources, and timeline details.
Discover Your AI Level
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1. The Hierarchy of Intelligence
The broad discipline of building machines that can perform tasks requiring human intelligence.
A subset where machines learn from data to perform specific tasks without explicit programming.
The specific ability to create new content (text, images, code) rather than just analyzing existing data.
2. The Generative AI Stack
To build a GenAI application, you need more than just a model. You need an entire ecosystem functioning in harmony.
Infrastructure
GPUs, TPUs, & Cloud Servers
Foundation Model
GPT-5 family, Gemini 3 family, Claude 4 family
Platform & Tools
APIs, Vector DBs, Frameworks
Agents & Apps
The user-facing interface
3. Agents: The Next Evolution
Chatbot vs. Agent
A standard chatbot answers questions based on training data. An Agent has agency—it can use tools, browse the web, and execute complex workflows to achieve a goal.
4. Controlling the Chaos
LLMs are probabilistic. You can control their "creativity" using specific parameters.
Temperature
0.0 - 1.0Hallucinations
Models do not "know" facts; they predict the next likely word. If they don't know the answer, they may confidently invent one.
5. Human in the Loop (HITL)
Why humans are still essential
AI is powerful but not infallible. For high-stakes decisions, humans must remain in the workflow.
High Risk Decisions
Medical, legal, or financial advice requires human sign-off.
Content Moderation
Catching subtle nuance or harm that algorithms miss.
Edge Cases
Handling rare scenarios the model wasn't trained on.
Now put it into practice
You understand the engine. Use proven prompting systems to get better outputs every time.