#prompt-engineering

Measure which prompt performs better. Features: 1. Two versions of a prompt (Variant A vs B). 2. Key Metrics: Relevancy Score, Accuracy, Response Speed, Token Usage. 3. Statistically significant 'Winner' badge. 4. User feedback collection tool for manual evaluation. 5. Chart comparing costs over 1,000 runs.

Master generative AI and large language model development, fine-tuning, and deployment for various applications. LLM architecture fundamentals: 1. Transformer architecture: self-attention mechanism, multi-head attention, positional encoding. 2. Model scaling: parameter count (GPT-3: 175B), training data (tokens), computational requirements. 3. Architecture variants: encoder-only (BERT), decoder-only (GPT), encoder-decoder (T5). Pre-training strategies: 1. Data preparation: web crawling, deduplication, quality filtering, tokenization (BPE, SentencePiece). 2. Training objectives: next token prediction, masked language modeling, contrastive learning. 3. Infrastructure: distributed training, gradient accumulation, mixed precision (FP16/BF16). Fine-tuning approaches: 1. Supervised fine-tuning: task-specific datasets, learning rate 5e-5 to 1e-4, batch size 8-32. 2. Parameter-efficient fine-tuning: LoRA (Low-Rank Adaptation), adapters, prompt tuning. 3. Reinforcement Learning from Human Feedback (RLHF): reward modeling, PPO training. Prompt engineering: 1. Zero-shot prompting: task description without examples, clear instruction formatting. 2. Few-shot learning: 1-5 examples, in-context learning, demonstration selection strategies. 3. Chain-of-thought: step-by-step reasoning, intermediate steps, complex problem solving. Evaluation methods: 1. Perplexity: language modeling capability, lower is better, domain-specific evaluation. 2. BLEU score: text generation quality, n-gram overlap, reference comparison. 3. Human evaluation: quality, relevance, safety assessment, inter-rater reliability. Deployment considerations: inference optimization, model quantization, caching strategies, latency <1000ms target, cost optimization through batching.

Designed to take a messy user prompt and 'optimize' it for GPT-4o. The optimizer should add: 1. Specific Persona (Expert Scientist, Creative Writer). 2. Constraints (No jargon, under 200 words). 3. Step-by-step reasoning instructions. 4. Expected output format (JSON, Markdown). 5. Few-shot examples to guide the model.

Optimize prompts for Claude. Techniques: 1. Use XML tags for structure (<document>, <instructions>). 2. Human/Assistant message format. 3. Chain-of-thought prompting. 4. Few-shot examples for context. 5. System prompts for behavior. 6. explicit instructions format. 7. Handle 100k+ token context. 8. Streaming for long outputs. Claude excels at following instructions precisely. Implement constitutional AI principles.

Master Midjourney prompts for art. Techniques: 1. Descriptive subject and style. 2. Parameters (--ar, --v, --s, --q). 3. Multi-prompts with :: weights. 4. Image prompts for style reference. 5. Negative weights to exclude. 6. Chaos for variety. 7. Stylize for artistic interpretation. 8. Seeds for reproducibility. Use /imagine command and iterate with variations.