How to Build an LLM in a Loop Workflow | Human, AI, and Self-Evaluation Explained | CodeAuris
How to Build an LLM in a Loop Workflow | Human, AI, and Self-Evaluation Explained | CodeAuris
In this hands-on tutorial, we take a deep dive into one of the most powerful LLM workflow patterns — LLM in a Loop. Building on our previous video about LLM design patterns, we explore how placing a language model inside a feedback loop enables iterative improvement, resulting in more accurate, reliable, and useful outputs.
Code - https://github.com/codeauris/codeauri...
🚀 What You’ll Learn in This Video:
✔️ The core concept behind LLM in a Loop
✔️ Why a single LLM output is often not enough
✔️ Three types of feedback loops: Human-in-the-Loop, AI-in-the-Loop, Self-Looping
✔️ Types of evaluations: Rule-based Evaluation, LLM-based Evaluation, Real-World Data Evaluation
🔧 Live Coding Walkthrough:
We'll go beyond theory and show you how to implement a working LLM loop in Python using the Grok LLM API. You'll learn to:
✔️ Set up a Correction Agent to iteratively refine a document
✔️ Build an Evaluation Agent to provide feedback
✔️ Manage step-by-step document improvement
✔️ Save and log every iteration to track progress
📂 The full workflow includes:
✔️ Correction and evaluation instructions
✔️ A helper function to interact with Grok LLM
✔️ A retry mechanism for invalid JSON
✔️ A structured file output system to monitor changes
✔️ A final document version improved through multiple iterations
📄 Example Use Case:
We apply the LLM loop to a documentary script, showing how each loop improves grammar, clarity, tone, and overall structure. By the end of the video, you’ll see how human or AI feedback can turn raw LLM output into polished, production-ready content.
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⏱️ Video Timestamps – What are AI Agents: Key Features and How They Differ from ChatGPT & Other AI Tools
00:00 - What is LLM in a Loop? Explained with Use Cases
00:45 - Why Use Feedback Loops with LLMs?
01:19 - How LLM in a Loop Workflow Works (Step-by-Step Guide)
01:47 - Benefits of LLM in a Loop: Iterative Refinement Explained
02:14 - Types of LLM Feedback Loops: Human, AI & Self Looping
02:30 - Human-in-the-Loop for LLMs: Examples & Benefits
03:49 - AI-in-the-Loop: Automating LLM Feedback Without Humans
04:56 - Self-Looping LLM: How Models Can Self-Improve
06:01 - Evaluating LLM Output: Why It Matters in Feedback Loops
06:08 - Rule-Based Evaluation for LLMs: Fast & Automated Checks
07:45 - LLM-as-a-Judge: How LLMs Evaluate Each Other's Output
08:56 - Real-World Data Evaluation: Using Engagement Metrics to Refine LLMs
10:43 - Agents Used in LLM in a Loop: Correction and Evaluator
12:22 - Correction Script: How It Applies Document Improvements
13:44 - The process_correction_step Function Explained
14:18 - Breaking Down the Evaluation Agent Workflow
15:35 - Document Correction Loop Function – Main Engine
16:34 - Three Feedback Loops Per Step: Improving Precision
17:00 - Evaluation Feedback Controls Retry or Next Step
17:59 - Reviewing Improvements Across Iterative Steps
18:10 - Final Thoughts and Recap on LLM in a Loop Workflow
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