This document provides a comprehensive analysis of the AI Engineering course material, which demonstrates a simple workflow for building, evaluating, and improving AI systems with Retrieval Augmented Generation (RAG), agents, and evaluation. The analysis examines the implementation details, architecture, and progressive improvement pattern that characterizes modern AI application development.
graph TD
A[Data Infrastructure] --> B[Retrieval Systems]
B --> C[Generation & Reasoning]
You are an AI researcher with expertise in large language models and structured data formats. Your task is to produce a comprehensive literature survey and experimental plan investigating the following question:
“How do contemporary transformer‐based LLMs (as of May 2025) differentiate—if at all—between generating freeform natural language and instantiating a structured schema (e.g., JSON, XML, SQL)? What mechanisms (training data patterns, positional encodings, constrained decoding, function‐calling APIs) contribute to their ability to produce schema‑compliant outputs, and where do failure modes most often occur?”
Please include:
llms.txt standard on developer workflows.llms.txt.Source Report: https://chatgpt.com/s/dr_68157dbaf944819185d8bf95fee71e1d
This document provides a comprehensive overview of the AI Makerspace Unsloth GRPO Training notebook, which demonstrates how to implement a simplified version of the DeepSeek-R1 training process.
Group Reinforcement Policy Optimization (GRPO) is an innovative approach to training language models that focuses on rewarding desired behaviors rather than providing explicit examples. The process involves:
Now I'll create the two requested artifacts: a sequence diagram and a markdown file describing the end-to-end flow.
I've created two detailed artifacts that describe the GRPO training pipeline implemented in the notebook:
Sequence Diagram: The first artifact is a comprehensive sequence diagram showing the interactions between various libraries and components in the GRPO pipeline. It illustrates the flow from installation through model loading, data preparation, reward function setup, training configuration, the training process itself, and finally inference.
End-to-End Flow Documentation: The second artifact is a detailed markdown document explaining each step of the process with code examples. It covers:
This document provides a comprehensive overview of the AI Makerspace Unsloth GRPO Training notebook, which demonstrates how to implement a simplified version of the DeepSeek-R1 training process.
Group Reinforcement Policy Optimization (GRPO) is an innovative approach to training language models that focuses on rewarding desired behaviors rather than providing explicit examples. The process involves:
The open_deep_research application is designed to generate structured research reports by leveraging LangGraph, a graph-based workflow, to plan, research, and compile sections of a report. Here’s a quick breakdown of the files and their relevance to pitch deck creation:
StateGraph. It orchestrates the process of generating a report plan, gathering human feedback, researching sections via web searches, writing sections, and compiling a final report. Key nodes include:
generate_report_plan: Creates a structured plan with sections based on the topic, using web searches for context.human_feedback: Allows for iterative refinement of the plan.build_section_with_web_research: A subgraph that generates queries, searches the web, writes sections, and evaluates quality.write_final_sections and compile_final_report: Handle non-researched s
Welcome to this walkthrough on fine-tuning embedding models to enhance Retrieval-Augmented Generation (RAG) systems. This guide will walk you through the steps to fine-tune an embedding model on domain-specific data, evaluate its performance, and implement it into a RAG pipeline.
The core idea behind fine-tuning embeddings is simple:
Move the embeddings for questions relating to a document closer together with that document
Welcome to this walkthrough on fine-tuning embedding models to enhance Retrieval-Augmented Generation (RAG) systems. This guide will walk you through the steps to fine-tune an embedding model on domain-specific data, evaluate its performance, and implement it into a RAG pipeline.
The core idea behind fine-tuning embeddings is simple:
Move the embeddings for questions relating to a document closer together with that document