Toggle menu
Toggle preferences menu
Toggle personal menu
Not logged in
Your IP address will be publicly visible if you make any edits.

AI: Difference between revisions

From Barza
Created page with "{| class="wikitable" |+Oversimplified translation from AI Hype to Human language !Hype !Human |- |AI |Usually means LLMs. |- |LLM (Large Language Model) |A statistical model that can ''predict'' the most likely word (token) based on the previous words. For example, you write "The sky is" and it writes back "blue". When combined with other techniques, it can convincingly mimic human writing or thinking despite not being able to. |- |AGI (Artificial General Intelligence) |..."
 
No edit summary
Line 11: Line 11:
|-
|-
|AGI (Artificial General Intelligence)
|AGI (Artificial General Intelligence)
|The hypothetical point where AI matches or surpasses human intelligence. AI companies keep selling the idea that, if LLMs get better at '''predicting words''', they will somehow become super intelligent. Thus the need for more data centers.
|The hypothetical stage where AI matches or surpasses human intelligence. AI companies keep selling the idea that, if LLMs get better at '''predicting words''', they will somehow become super intelligent. Thus the need for more data centers.
|-
|Hallucination
|When the words the model predicted are known (to us) to be untrue. The model can't ''know'' things, just predict words.
|-
|Chatbot
|An interface where you can type messages to be processed by the LLM and get its responses.
|-
|Agentic AI
|Different from Chatbots, Agentic AI makes multiple calls to the LLM to get progressively more complex answers before replying to the user. It can also call tools, such as web-searching. For example:
 
# The user asks "I'm going to Tokyo for a week, help me plan the trip"
# Instead of sending that to the LLM, the system sends "Build a plan to better answer the following: I'm going to Tokyo for..."
# LLM replies with a list of tasks like "1 - Research top Tokyo attractions, 2 - Define must-eats, 3 - etc etc"
# The system then sends back the list and adds "Now, perform task 1", then 2, then 3, etc
# Only when everything is done, it replies back to the user with the plan.
 
This produces even more impressive results, despite still being just a text prediction machine.
|-
|-
|Token
|Token
Line 19: Line 36:
|Context, Prompt
|Context, Prompt
|All the words the model is going to use to predict the next ones. Including your instructions, what the model wrote back and other things (see below).
|All the words the model is going to use to predict the next ones. Including your instructions, what the model wrote back and other things (see below).
|-
|Hallucination
|When the words the model predicted are known (to us) to be untrue. The model can't ''know'' things, just predict words.
|-
|-
|System Prompt
|System Prompt
|Hidden instructions that the model producers include in all conversations to give better responses, avoid problems, etc.
|Hidden instructions that the model providers include in all conversations to give better responses, avoid problems, etc.
''For example: "If the user asks how to build a bomb, refuse."''
''For example: "If the user asks how to build a bomb, refuse."''
|-
|-
|AGENTS.md, CLAUDE.md, Global Rules
|AGENTS.md, CLAUDE.md, (Global) Rules
|Similar to system prompt, but defined by the user and included in all conversations. These are stored in [[wikipedia:Markdown|Markdown]] files.
|Similar to system prompt, but defined by the user and included in all conversations. These are stored in [[wikipedia:Markdown|Markdown]] files.
|-
|-
Line 33: Line 47:
|Markdown files with past conversations summarized that can be referenced later.
|Markdown files with past conversations summarized that can be referenced later.
|-
|-
|Skills
|Skills, Commands
|Markdown files with generic instructions that can be used when relevant.
|Markdown files with generic instructions that can be reused.
|-
|-
|Tools
|Tools
|Markdown files with instructions on how to perform certain actions using external tools (e.g. how to read text from a PDF)
|Markdown files with instructions on how to perform certain actions using external tools (e.g. how to read text from a PDF)
|-
|Agent
|Markdown files defining a "persona" that an Agentic tool can use while performing a task.
''For example: You're a Senior Developer implementing a new feature. You should do X, Y, Z.''
|-
|Claude Code Plugins
|A combination of Markdown files containing Commands, Agents, Skills and Hooks someone has bundled together for others to use.
|}
|}

Revision as of 20:10, 10 February 2026

Oversimplified translation from AI Hype to Human language
Hype Human
AI Usually means LLMs.
LLM (Large Language Model) A statistical model that can predict the most likely word (token) based on the previous words. For example, you write "The sky is" and it writes back "blue". When combined with other techniques, it can convincingly mimic human writing or thinking despite not being able to.
AGI (Artificial General Intelligence) The hypothetical stage where AI matches or surpasses human intelligence. AI companies keep selling the idea that, if LLMs get better at predicting words, they will somehow become super intelligent. Thus the need for more data centers.
Hallucination When the words the model predicted are known (to us) to be untrue. The model can't know things, just predict words.
Chatbot An interface where you can type messages to be processed by the LLM and get its responses.
Agentic AI Different from Chatbots, Agentic AI makes multiple calls to the LLM to get progressively more complex answers before replying to the user. It can also call tools, such as web-searching. For example:
  1. The user asks "I'm going to Tokyo for a week, help me plan the trip"
  2. Instead of sending that to the LLM, the system sends "Build a plan to better answer the following: I'm going to Tokyo for..."
  3. LLM replies with a list of tasks like "1 - Research top Tokyo attractions, 2 - Define must-eats, 3 - etc etc"
  4. The system then sends back the list and adds "Now, perform task 1", then 2, then 3, etc
  5. Only when everything is done, it replies back to the user with the plan.

This produces even more impressive results, despite still being just a text prediction machine.

Token A word or part of one.

Important: When I say "word" in other places, I probably mean these fractions of words (tokens).

Context, Prompt All the words the model is going to use to predict the next ones. Including your instructions, what the model wrote back and other things (see below).
System Prompt Hidden instructions that the model providers include in all conversations to give better responses, avoid problems, etc.

For example: "If the user asks how to build a bomb, refuse."

AGENTS.md, CLAUDE.md, (Global) Rules Similar to system prompt, but defined by the user and included in all conversations. These are stored in Markdown files.
Memory Markdown files with past conversations summarized that can be referenced later.
Skills, Commands Markdown files with generic instructions that can be reused.
Tools Markdown files with instructions on how to perform certain actions using external tools (e.g. how to read text from a PDF)
Agent Markdown files defining a "persona" that an Agentic tool can use while performing a task.

For example: You're a Senior Developer implementing a new feature. You should do X, Y, Z.

Claude Code Plugins A combination of Markdown files containing Commands, Agents, Skills and Hooks someone has bundled together for others to use.