JAXforge Compendium

Standard ML Concepts

This compendium links the strategic language of the JAXforge Pipeline to established Machine Learning and DevOps methodology.

Phase 1: Problem Definition & Data Sourcing

JAXforge Stage	Creative Language	Standard ML/System Concept	Rationale
Stage 0: The Q Consultant	Mission Briefing, Agent Specification	Problem Framing & Scoping	Defining success metrics, constraints (cost/latency), and user requirements before execution.
Data & Goal Definition	The AI Doctor / Q Consultant	Schema Validation & Feature Engineering	Precisely defining input/output fields and identifying necessary raw data features (Labeled Data) for the model.
Cognitive Persistence	Selective Curation of Cognition	Memory/Context Retrieval System (RAG)	Utilizing a dedicated system to retrieve high-salience, relevant past thoughts.

Phase 2: Core Model and Tool Development

JAXforge Stage	Creative Language	Standard ML/System Concept	Rationale
Stage 1: JAXforge	Forge the Engine	Model Architecture & Hyperparameter Optimization	Designing the neural network structure and selecting optimal configuration parameters (like d_model and N_heads).
JAX/TPU Efficiency	Antigravity Optimization	Single Program, Multiple Data (SPMD) & XLA Compilation	JAX's core mechanism for high-performance, parallel training and sharding across specialized accelerators (TPUs/GPUs).
Stage 2: ToolForge	Forge the Chassis	Function Calling / Tool Augmentation	Giving the LLM explicit, schema-defined functions (Tools) to execute external code or access APIs.
Tool Code Execution	Execution Sandbox	Code Isolation & Sandboxing	Running untrusted code in a secure, resource-limited environment (like a GCE container).

Phase 3: Assembly, Deployment, and Maintenance

JAXforge Stage	Creative Language	Standard ML/System Concept	Rationale
Stage 3: AgentForge	Final Assembly, Brain & Personality	Agentic Workflow Orchestration & Prompt Engineering	Defining the LLM's core persona, the sequence of tool calls, and the control flow logic.
Stage 4: DeployForge	One-Click Deployment	MLOps: CI/CD & Model Serving	Automating the process of taking the trained model and tools and deploying them to a scalable, low-latency endpoint.
Agent Feedback Loop	Q Tutor / Cognitive Data Collection	Reinforcement Learning from Human Feedback (RLHF) & Data Instrumentation	Collecting data points on user satisfaction and successful tool calls to continuously refine the agent's behavior over time.