#llm Prompts

Build enterprise-grade LLM fine-tuning system. Pipeline: 1. Implement data preprocessing and quality validation. 2. Set up LoRA (Low-Rank Adaptation) for efficient training. 3. Configure distributed training across multiple GPUs. 4. Implement gradient checkpointing for memory optimization. 5. Add au...

Professional diagram following Retrieval Augmented Generation architecture. Components: 1. Document Loader -> Splitting -> Embeddings. 2. Vector DB Storage. 3. Query Rewrite -> Retrieval -> Re-ranking. 4. Contextual Prompt -> LLM Generation. Use blue/violet gradients and high-quality technical icons...

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 ...

A tool to auto-generate Hugging Face model cards. Sections to include: 1. Model Description (Architecture, Parameters). 2. Training Data (Datasets used). 3. Evaluation Results (MMLU, HumanEval scores). 4. Intended Use and Biases. 5. Citation info. Minimalist layout with badges for 'Transformers', 'P...

A leaderboard-style comparison of different fine-tuned models. Compare: 1. Llama 3 (LoRA) vs GPT-4v (RLHF) vs Mistral (Base). 2. Benchmarks: MMLU, GSM8k, HumanEval. 3. Column to show 'Inference Cost' vs 'Accuracy'. 4. Radar chart for multi-dimensional performance analysis.

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. ...

Build RAG systems with LlamaIndex. Workflow: 1. Load documents (PDF, DOCX, web). 2. Node parser for chunking. 3. Create embeddings with LLM. 4. Build index (Vector, Tree, Keyword). 5. Query engine for retrieval. 6. Response synthesizer. 7. Sub-question query engine. 8. Chat engine for conversations....

Integrate GPT-4 API effectively. Patterns: 1. Chat completions with system/user messages. 2. Function calling for structured outputs. 3. Streaming responses for better UX. 4. Token counting to manage costs. 5. Temperature and top_p tuning. 6. Max tokens control. 7. Error handling and retries. 8. Rat...

Visualize a complex LangChain agent flow. Flow components: 1. User Input -> Embedding Model. 2. Vector DB (Pinecone) retrieval. 3. LLM (GPT-4) reasoning step. 4. Tool execution (Google Search, Python Repl). 5. Final Output. Use a node-based diagram style with directed arrows and color-coded componen...

Get structured data from LLMs with Instructor. Pattern: 1. Define Pydantic models for output. 2. Use instructor.patch() on OpenAI client. 3. LLM returns validated objects. 4. Automatic retry on validation errors. 5. Partial streaming for progressive updates. 6. Union types for multiple formats. 7. N...