The Architecture (Questions 1, 2 & 9)
These questions focus on how a model is built and its physical limitations.
1. Parameters:
Answer: Internal weights and settings that define the model's structure and intelligence.
Concept: Think of these as the "knobs" the model adjusts during training. More parameters often equal a more capable (but slower) model.
2. Context Window Limit:
Answer: The model drops the earliest information to make room for new data, potentially leading to hallucinations.
Concept: Like short-term memory. Once it’s full, the "oldest" info is deleted so it can keep talking, which can cause it to lose track of original instructions.
9. High-Volume/Low-Latency Tasks:
Answer: Small Language Models (SLMs).
Concept: If you need speed and repetition over deep reasoning, a smaller, lighter model is faster and cheaper than a massive "Frontier" model.
Enterprise Strategy (Questions 3, 4 & 8)
These focus on how businesses actually use AI to gain an advantage.
3. The Competitive Moat:
Answer: Connecting GenAI to unique, proprietary data and domain expertise.
Concept: Everyone has the model; not everyone has your company's private data. That's the secret sauce.
4. RAG (Retrieval-Augmented Generation):
Answer: It allows the model to look up real-time information from external trusted sources before generating an answer.
Concept: The "Open Book" method. It searches your files first, then answers based on what it found.
8. Grounding:
Answer: It anchors the model's responses in specific, verified organizational data to reduce hallucinations.
Concept: Ensuring the AI "stays in its lane" by forcing it to use specific, verified facts rather than guessing.
Agents & Reasoning (Questions 5, 7 & 10)
These look at how AI moves from "chatting" to "doing."
5. GenAI vs. AI Agents:
Answer: GenAI is for single-step generation, while agents use reasoning for multi-step, adaptive workflows.
Concept: GenAI is a calculator; an Agent is a mathematician who knows which buttons to press to solve a long word problem.
7. The Intelligent Router:
Answer: Supervisor Agent Brick.
Concept: The "Manager." It listens to your request and decides which "specialist" (sub-agent) is the right one to fix it.
10. The "Brilliant Intern" Analogy:
Answer: Highly knowledgeable but takes instructions extremely literally and lacks specific business context.
Concept: You have to be specific. It’s smart, but it doesn't know your company's "unspoken" rules yet.
Evaluation & Bias (Question 6)
How we measure if the AI is actually doing a good job.
6. LLM-as-a-Judge (The "Con"):
Answer: It may exhibit "verbosity bias," favoring longer responses regardless of accuracy.
Concept: AI judges often fall for "fluff." They might give a higher grade to a long, poetic answer than a short, 100% correct one.
Quick Reference Comparison
| Feature | Standard GenAI | AI Agent |
| Workflow | Single-turn (Input $\rightarrow$ Output) | Multi-step (Plan $\rightarrow$ Tool $\rightarrow$ Result) |
| Memory | Context Window | Context + Long-term "Memory" storage |
| Data Access | Training Data (Static) | RAG / Grounding (Real-time) |
| Logic | Pattern Recognition | Iterative Reasoning |