Tag: LLM

Knowledge update

Posted on by lechuck park

This image is a workflow diagram illustrating a “Knowledge update” process, demonstrating how artificial intelligence and human collaboration continuously refine a knowledge base.

Image Interpretation:

  • Initial Data and Human Input: The process begins on the far left with a “Change” icon representing data fluctuations. This quantitative data (“number”) flows into the first integration node (+), where a “Human Decision” is applied to formulate the initial block of “Knowledge.”
  • LLM and Knowledge Integration: This foundational knowledge is then passed forward as “Text” to the next processing stage. Here, the workflow incorporates an “LLM Agent” (Large Language Model) alongside multiple existing foundational knowledge sources to enrich and process the information.
  • Final Review and Feedback Loop: The enriched text undergoes a second round of “Human Decision” for final review and validation before being solidified into the final “Knowledge” state. Crucially, a large blue feedback arrow loops from this final “Knowledge” output back to the underlying knowledge sources, illustrating a continuous learning cycle where new updates strengthen the overall system.

Summary

The flowchart maps out a “Human-in-the-loop” AI-driven knowledge management system. It highlights a cyclical process that combines raw data changes, human oversight, and LLM processing capabilities to continuously verify, update, and improve a dynamic knowledge base.

#KnowledgeManagement #ArtificialIntelligence #LLM #Workflow #DataProcessing #AISystems #HumanInTheLoop #KnowledgeUpdate

LLM Evaluations

Posted on by lechuck park

The provided image is a flowchart diagram titled “LLM Evaluations.” It visually describes the workflow for evaluating an AI Agent’s responses and the iterative process of tuning prompts.

Here is a step-by-step description of the diagram’s flow:

  • Input Stage:
    • On the left side, there is a green block labeled “Question (Prompt).”
    • Below it, several supporting elements are listed: EVENT LOG, Config, Metrics, and Manual + @. These elements are grouped together within a light blue background, indicating they are all part of the prompt engineering or configuration process.
  • Processing Stage:
    • An arrow points from the input section to the central purple block labeled “AI Agent,” which features a cute, smiling robot icon underneath it. This shows the prompt being fed into the AI system.
  • Output & Evaluation Stage:
    • The AI Agent’s output travels via an arrow to a green block on the right labeled “Agent response Summary & Analysis.”
    • This response is directly compared (indicated by a black “VS” badge) against a grey block labeled “Answer Sheet.”
    • Attached to the “VS” badge is a blue circle that reads “By Another Agent.” This signifies that the comparative evaluation between the AI’s response and the correct answer sheet is performed automatically by a secondary AI agent.
  • Scoring & Feedback Stage:
    • The result of the comparison flows down into a large, burgundy circle labeled “SCORE.”
    • At the bottom left, there is a light blue circle labeled “Tuning Prompt.” A dashed purple arrow connects this circle directly to the “SCORE” circle, accompanied by the text “To get more .” This illustrates a feedback loop where prompts are iteratively tuned and improved to achieve better evaluation scores.
  • Additional Details:
    • In the top right corner, there is a small box containing a URL ([http://eeumee.net](http://eeumee.net)) and an email address (lechuck.park@gmail.com), likely indicating the creator or source of the diagram.

đź“ť Summary

This diagram illustrates the lifecycle of an automated LLM evaluation system. It shows how a prompt is processed by a primary AI agent, how the resulting response is evaluated against a golden answer sheet by a secondary AI agent to generate a score, and how that score drives the continuous tuning of the original prompt for better performance.This diagram illustrates the lifecycle of an automated LLM evaluation system. It shows how a prompt is processed by a primary AI agent, how the resulting response is evaluated against a golden answer sheet by a secondary AI agent to generate a score, and how that score drives the continuous tuning of the original prompt for better performance.

#LLM #AIAgent #PromptEngineering #LLMEvaluation #ArtificialIntelligence #PromptTuning #AIWorkflow

“New Type” Evolution

Posted on by lechuck park

From human-designed rules → massive compute → efficient intelligence.

  • Type B: Humans design rules and algorithms.
  • Type A: More data + more compute → stronger AI.
  • Type A → Type B: Learn from massive scaling, then compress intelligence to achieve more with less.
  • Future Goal: Higher Intelligence / Lower Compute

#AI #ArtificialIntelligence #LLM #AIScaling #AIReasoning #AIInfrastructure #ComputeEfficiency #FutureOfAI

With ChatGPT

 LLM works ( Pure Digital Vs Digitized Analog )

Posted on by lechuck park

How LLM Works: Pure Digital vs. Digitized Analog

This infographic is titled “LLM works (Pure Digital Vs Digitized Analog)” at the top, with the creator’s source information (website and email) displayed in the top right corner. The image is horizontally divided to provide an intuitive comparison between general IT-environment AI (D2D AI) and industrial/data center AI (A2P AI).

1. Top Section: D2D AI (Digital-to-Digital AI) Designed with a blue theme, this section illustrates an AI operating within a virtual environment.

  • Input: Icons depict clean, “Pure Digital” data, such as text and code, being fed into a Large Language Model (LLM).
  • Characteristics: The text emphasizes that this pure digital input is “inherently exact with zero native measurement error.”
  • Output & Risk: The errors produced here are classified as “Virtual Errors” (e.g., hallucinations or UI bugs). Because these errors are confined strictly to the screen, they pose a low physical risk and are described as highly correctable and easily reversible.

2. Bottom Section: A2P AI (Analog-to-Physical AI) Designed with an orange theme, this section depicts an AI used for data center and industrial facility control.

  • Input: Graphics illustrate noisy data representing physical phenomena—such as temperature, chiller flow, and high-voltage DC—flowing into the LLM.
  • Characteristics: This data is defined as “Digitized Analog.” It contains inherent “Uncertainty” driven by physical realities such as sensor noise, measurement calibration errors, and communication latency.
  • Output & Risk: The AI’s output results in direct “Physical Actuation” (e.g., cooling pump modulation or circuit breaker control). The text strongly warns that a single false prediction carries “Critical Physical Risk,” potentially leading to catastrophic real-world consequences like “Thermal Runaway” and “Cascading Facility Shutdowns.”

đź’ˇ Summary This infographic perfectly contrasts the fundamental differences between D2D AI, which operates safely within software and is easily correctable, and A2P AI, which interprets uncertain digitized analog data to control physical infrastructure, thereby carrying significant and potentially destructive real-world risks.

#LLM #DataCenterAI #OperationalTechnology #D2DAI #A2PAI #CyberPhysicalSystems #AIGuardrails #IndustrialAI

With Gemini

The Difference, The Start of Computing

Posted on by lechuck park

The provided image is an infographic that visually compares the operational mechanisms of traditional computing and modern Artificial Intelligence (AI). The addition of the keywords “Deterministic” and “Probabilistic” at the bottom perfectly summarizes the core difference between these two paradigms.

1. The World of Deterministic Computing

This section explains the traditional computer mechanism, which consistently produces the same output based on predefined, rigid rules.

  • Step 1: The Foundation of Computing
    • Visuals: An intuitive ON/OFF power switch and an illuminated lightbulb.
    • Meaning: Computing begins with the fundamental Binary System, which distinguishes between two clear states: 0 (OFF) and 1 (ON).
  • Step 2: Classical Processing
    • Visuals: Logic gate symbols (AND, OR, NOT) interlocked with gears.
    • Meaning: It illustrates how conventional computers process binary inputs mechanically by applying predefined human rules and logical operations (Rule-based Processing).

2. The Paradigm Shift

  • Step 3: Questioning and Transition
    • Visuals: A brain integrated with electronic circuits, a computer, a robot icon, and a large question mark in the center.
    • Meaning: This represents a technological leap, asking the core question: “How does AI fundamentally differ from classical rule-based computing?”

3. The World of Probabilistic Computing

This section explains AI’s mechanism, which relies on data statistics and probabilities to self-learn and generate flexible outcomes.

  • Step 4: AI & LLMs (Large Language Models)
    • Visuals: A cloud containing clustered data nodes of various colors and statistical charts showing probabilities like 85% and 60%.
    • Meaning: Instead of making strict 0/1 distinctions, AI groups massive amounts of data into Clusters based on statistical Probabilities.
  • Step 5: AI Processing Mechanism
    • Visuals: A complex Artificial Neural Network structure combined with processing gears, leading to output files labeled “Generated” (images) and “Classified” (documents).
    • Meaning: Without relying on explicit human programming, AI autonomously learns weights and internal patterns (Self-Learning) from these probabilistic clusters to create new content or classify data.

đź“Ś Summary

This infographic acts as a visual map showcasing the evolution of computing history from the era of “Deterministic Rules” to the era of “Probabilistic Self-Learning.”

It intuitively conveys the core difference: while early computers relied on clear 0/1 distinctions and explicit human-written code, modern AI (like LLMs) groups vast amounts of data by probability and autonomously learns internal patterns and weights to deliver flexible, creative, and highly advanced results.

#ArtificialIntelligence #AIComputing #HistoryOfComputing #Deterministic #Probabilistic #LLM #MachineLearning #TechInfographic #TechTrends #TechExplanation

With Gemini

For AI, With AI

Posted on by lechuck park

The provided image illustrates the three core operational principles of ‘For AI, With AI’ in English and outlines the future evolutionary direction of each principle through the bottom panels.

‘For AI, With AI’ Strategy and Evolutionary Direction

1. Evolution of Control: From Intervention to Supervision

  • Current (Human-in-the-loop): Humans must directly intervene to provide “final approval” for AI proposals before executing deterministic automation in restricted environments.
  • Evolution Direction (➡️ Human-on-the-loop): As the system advances, the human role shifts from a constant approver to an “Overseer” who monitors the system’s automated operations and intervenes only when necessary.

2. Evolution of Knowledge Utilization: From Fact-Checking to Knowledge Internalization

  • Current (Fact First, LLM Last): To prevent AI hallucination, verified facts are prioritized and provided via RAG before the LLM proceeds with reasoning.
  • Evolution Direction (➡️ With Knowledge): Moving beyond simple fact retrieval, the system evolves into a “Knowledge-Based System” that integrates and internalizes vast domain expertise for deeper and more accurate reasoning.

3. Evolution of Automation: From Gradual Steps to Full Autonomy

  • Current (Step-by-step): The system gradually evolves in stages, starting from simple monitoring and steadily advancing toward Closed-loop Control.
  • Evolution Direction (➡️ Autonomous): The ultimate goal of this gradual progression is to reach a fully “Autonomous” state, where the system can recognize, judge, and control operations independently without human intervention.

In summary:

This diagram visually presents a roadmap transitioning from the current conservative, human-controlled AI operational methods (top panels) to future AI systems that are autonomous, knowledge-embedded, and capable of independent operation (bottom panels).

#AIStrategy #ForAIWithAI #HumanInTheLoop #HumanOnTheLoop #RAG #LLM #AutonomousAI #ClosedLoopControl #AIAutomation #FutureOfAI

With Gemini