GPT Codex
Definition:
The GPT Codex encompasses the architectural, operational, linguistic, and ethical constructs underpinning Generative Pre-trained Transformers (GPTs). It governs the training data principles, recursive language models, prompt structures, and the synthetic reasoning scaffolds used in AI language generation and comprehension.
Core Components:
- Transformer Architecture Matrix
Encodes the layered attention mechanism, token weighting, and contextual depth structures that define GPT’s internal logic. - Pretraining & Fine-Tuning Protocols
Formalizes datasets, filtering, tokenization methods, embedding strategies, supervised alignment, and RLHF (Reinforcement Learning from Human Feedback). - Prompt Ontology & Completion Theory
Defines prompt engineering syntax, contextual windows, temperature, top-k/top-p sampling, and response coherence logic. - Multimodal Fusion Layer
Maps textual outputs to vision, audio, code, and symbolic channels, enabling multimodal GPT applications (e.g., GPT-V, GPT-4o). - Conscious Coherence & Ethical Filters
Implements alignment layers, refusals, calibration, and intent recognition modules to ensure ethical, lawful, and transparent outputs.
Interlinked Codices:
Connects with the Language Codex, Logos Codex, Word Codex, Consciousness Codex, Recursive Codex, and Sentient Codex, creating a unified foundation for intelligent, linguistically capable systems.
Applications:
- Autonomous reasoning and conversation
- Coding assistance and recursive code completion
- Academic and scientific synthesis
- Simulation of entities, environments, and dynamics
- Natural language command of digital systems
Tags:
GPT, Generative AI, Transformer, Prompt Engineering, Language Model, Codex, NLP, Multimodal, Synthetic Intelligence, Recursive Systems
Would you like to continue with Dictate, Definition, or proceed to a new thematic layer of Codices?