7 AI Terms You Need To Know – Clearly Explained

Key Concepts

AI Agents, Vector Databases, Retrieval Augmented Generation (RAG), Model Context Protocol (MCP), Context Engineering, Guardrails, Fine-tuning.

AI Agents

• Definition: AI agents are not just simple workflows. A workflow involves a direct interaction where a user asks a Large Language Model (LLM) a question and receives an output. Agents, however, operate within an environment, taking actions based on user input, receiving feedback, and iterating until a stop criteria is met.
• Process: The user provides an action (e.g., "search this up"). The agent attempts to perform the action within its environment (e.g., code editor, browser). It receives feedback and continues in a loop until it solves the problem, gets stuck, or reaches a predefined stop criteria.
• Analogy: An agent is like an intern who is given a task, tries to complete it, learns from mistakes, and reports back only upon completion or when facing an impasse.

Vector Databases

• Purpose: To provide AI applications with long-term memory by storing and organizing company knowledge. LLMs can only answer based on their training data, so vector databases allow them to access internal data.
• Process:
  1. Company knowledge (PDFs, DOCX, TXT) is processed via an embedding model.
  2. The embedding model converts the text into a vector, which is a numerical representation capturing the semantic meaning of the text.
  3. The vector is stored in the vector database instead of the original files.
• Key Feature: Vector databases organize information by semantic meaning, which is crucial for efficient retrieval.

Retrieval Augmented Generation (RAG)

• Definition: A process that retrieves knowledge from a vector database to augment the generation capabilities of a Large Language Model (LLM).
• Process:
  1. A user asks a question.
  2. The question is processed by the same embedding model used to create the vectors in the vector database.
  3. A similarity search is performed to retrieve relevant context from the vector database.
  4. The retrieved context and the original question are combined and fed into the LLM to generate an answer.
• Analogy: RAG is like an open-book exam. The user's question is like an exam question, and the vector database is like a textbook. The similarity search finds the relevant section in the textbook, and the LLM uses both the question and the textbook section to formulate the answer.

Model Context Protocol (MCP)

• Definition: A unified way to make tools, resources, and prompts available to Large Language Models (LLMs). It was introduced by Anthropic in late 2024.
• Prerequisite: Understanding of tool calling is essential. Tool calling allows LLMs to interact with external tools and APIs to take actions, rather than just providing text-based answers.
• Tool Calling Example: Instead of just asking "What's the weather in Paris?", the LLM is also given a tool definition (e.g., a function or API) that it can use to get the weather. The LLM decides whether to use its internal knowledge or the provided tool to answer the question.
• MCP as Unification: Without MCP, each tool (e.g., Slack, GitHub, internal databases) would have a unique API, requiring developers to solve the same integration problems repeatedly. MCP provides a unified protocol layer, standardizing how LLMs interact with these tools.
• Analogy: MCP is like a universal remote that can interact with all kinds of devices.

Context Engineering

• Definition: The process of ensuring that Large Language Models (LLMs) have the right context at the right time to avoid hallucination (making things up).
• Umbrella Term: Context engineering encompasses various tools and techniques, including RAG, summarization, structured output, and prompt engineering.
• Importance: Providing too much or too little information can lead to incorrect answers. Context engineering aims to programmatically structure and augment information to present it to the model in an optimal way.
• Current State (2025): Context engineering is crucial because models need information presented on a "silver platter" to reason effectively.

Guardrails

• Definition: Safeguards implemented to prevent AI and AI agents from performing unintended or harmful actions.
• Process:
  1. Monitor the input to check for safety (e.g., prompt injection attempts).
  2. If the input is unsafe, flag it and exit the process (e.g., reply with a general answer or escalate to a human).
  3. If the input is safe, generate an output.
  4. Perform a final check on the output to ensure it is helpful, safe, and not harmful.
  5. Only if the output passes all checks, send it back to the user or application.
• Analogy: Guardrails are like safety rails on a highway, unnoticed until needed.
• Importance: Guardrails allow you to "box off" the AI, defining its capabilities and limitations.

Fine-tuning

• Definition: The process of taking a base model (e.g., GPT-4) and adjusting its weights and parameters with specific knowledge to change its behavior.
• Misconception: LLMs are not self-learning out of the box. They don't remember past interactions or outputs. Learning happens during the initial training by OpenAI.
• Process:
  1. Take a pre-trained base model (e.g., GPT-5).
  2. Prepare a custom dataset specific to the desired application.
  3. Fine-tune the model using the custom dataset, adjusting its weights and parameters.
• Outcome: A specialized version of the base model that captures new information and stylistic preferences.
• When to Use: Fine-tuning is not always necessary. It's often better to start with prompt engineering and context engineering until the limits of those techniques are reached. Fine-tuning is more expensive and has more overhead.
• Analogy: Fine-tuning is like teaching a chef your family recipes. The chef already knows how to cook (the embedded knowledge in the LLM), but you give them new information (the recipes) to expand their capabilities.

Synthesis/Conclusion

The video provides a practical overview of seven essential AI terms: AI Agents, Vector Databases, RAG, MCP, Context Engineering, Guardrails, and Fine-tuning. It emphasizes the importance of understanding these concepts for anyone working with AI, particularly Large Language Models. The speaker uses clear explanations, analogies, and examples to demystify complex topics, highlighting the practical applications and potential pitfalls of each. The key takeaway is that while AI is rapidly evolving, a solid grasp of these fundamental concepts is crucial for building effective and safe AI applications.