AI, Unpacked: A Friendly Primer

• Updated: October 28, 2025

AI, Unpacked: A Friendly Primer for Our New Series

AI is moving fast, jargon even faster. Before we dive into deeper topics in this series, let’s align on a few core terms you’ll see everywhere. This post is your quick, no-fluff glossary: LLM vs. SML, prompts, agents, and MCP. We’ll keep it accurate, practical, and bias-free so you can navigate the rest with confidence.

LLM vs. SML

  • LLM (Large Language Model)

    • What it is: A neural network trained on massive text datasets to predict the next token in a sequence.
    • What it’s good at: Natural language tasks—chatting, summarizing, code generation, reasoning under uncertainty, pattern completion.
    • How it works (at a glance): Transformer architecture, self-attention, tokenization. It encodes context and produces probabilities over the next token.
    • Why it matters: LLMs are the foundation of modern AI assistants, coding copilots, and many “AI features” you use every day.

  • SML (Small Language Model)

    • What it is: A more compact language model with fewer parameters, designed for efficiency and specialization.
    • What it’s good at: On-device inference, low-latency tasks, privacy-sensitive workflows, domain-specific use-cases when fine-tuned well.
    • Trade-offs: Less broad knowledge and fewer “emergent” capabilities than LLMs, but cheaper, faster, and easier to deploy at the edge.

Think of LLMs as generalists with big brains; SMLs as tactical specialists you can run anywhere.

Prompt

  • What it is: The input you give a model to steer its behavior. It can be a question, instruction, system policy, examples, or a structured template.
  • Why it matters: Prompt design controls output quality. Structure, constraints, role-setting, and exemplars can drastically reduce hallucinations and improve reliability.
  • Practical components:
    • System message: High-level instructions and boundaries.
    • User message: The task or question.
    • Context: Documents, tools, or prior turns.
    • Examples: Few-shot demonstrations of desired behavior.
    • Constraints: Style, length, format, or schema requirements.
  • Tip: Treat prompts like APIs for natural language—be explicit, consistent, and test like you would code.

Agent

  • What it is: A model-augmented program that can plan and act. It doesn’t just generate text—it uses tools, calls APIs, reasons over steps, and loops until goals are met.
  • Core ingredients:
    • Policy/Planner: Decides what to do next (think: chain-of-thought, planning, or graph-based orchestration).
    • Tools: Functions the agent can call—search, code execution, database queries, emails, spreadsheets, etc.
    • Memory: Short-term scratchpads and long-term storage (vector databases, logs, knowledge bases).
    • Guardrails: Permissions, constraints, approval steps, and safety checks.
  • Why it matters: Agents bridge models and real work. They turn LLMs from “smart autocomplete” into workflow operators.

MCP (Model Context Protocol)

  • What it is: A protocol that standardizes how models (or agents) access tools, data sources, and capabilities. Think of it as a lingua franca for plugging models into your stack.
  • Why it matters: Without a standard, every tool integration is bespoke. MCP aims to make tooling interoperable, auditable, and safer across different models and runtimes.
  • Practical upside:
    • Unified interface for tools and data.
    • Better observability and permissions.
    • Portability: Swap models or runtimes without rewriting integrations.

How These Pieces Fit Together

  • You write a prompt to guide a model.
  • A small or large model responds based on its capabilities and context.
  • An agent wraps the model with planning, memory, and tools to execute multi-step tasks.
  • MCP provides a standard way for that agent/model to discover and securely use tools and data across systems.

Wrapping Up

AI terms don’t have to be mysterious. LLMs and SMLs describe model scale and trade-offs; prompts are how you steer behavior; agents turn models into doers with tools and memory; and MCP offers a clean way to standardize how models access your stack. Together, they form a practical toolkit for building reliable, efficient AI systems.

If this clicked for you, share it with someone who’s navigating the same concepts—and keep experimenting. The best way to learn this stuff is to put it to work.