LLM Pre-Training & Prompt Engineering Studio

1. LLM Training Pipeline Simulation

Simulate pre-training ingest, supervised tuning (SFT), and RLHF alignment.

Active: SFT

Corpus Ingest (GB) 500 GB

Learning Rate 1e-5

Self-Attention Token Salience Softmax(QK^T / sqrt(d_k))

Cross-Token Attention Matrix Heatmap Query vs Key Weights

Attention Entropy 0.142

Perplexity (PPL) 8.24

Curriculum Step 12,450 / 25k

2. Iterative Prompt Engineering Sandbox

Inject context, enforce negative constraints, and score model alignment.

Ready for deployment

Initial Weak User Prompt Vague context leads to autocomplete hallucination

Contextual Facts & Preservation Constraints Extracted background and explicit negative constraints

Aligned Production Prompt 94.5%

Compose a revised letter to Robert addressing vehicle liability and car damage claims. Specifically, remove Section II while strictly preserving all other sections and original tone.

+ Strict Section Isolation + Vehicle Liability Injected + Negative Constraint Guard + Preserved Persona

Alignment Evaluation Breakdown RLHF Reward Model Weights

Context Grounding & Specificity: 96.0%

Negative Constraint Retention (Section II guard): 98.2%

Hallucination / Entropy Penalty: -3.7%