Ai Llm

What it does

This skill assists with developing, evaluating, deploying, and operating Large Language Model (LLM) systems according to modern production standards. It covers the entire LLM lifecycle, from initial strategy selection and dataset design to ongoing quality monitoring and incident response. The focus is on treating models as components within a larger system, emphasizing cost awareness, security-by-design, and repeatable evaluation processes.

When to use it

• New product development: To guide the selection of LLM architecture (prompting, RAG, or fine-tuning).
• Model procurement decisions: To evaluate potential models based on quality, latency, cost, privacy, and licensing considerations using a scoring matrix.
• Cost optimization efforts: When needing to implement tiered model usage and caching strategies to minimize expenses per successful outcome.
• Fine-tuning investments: To analyze the return on investment (ROI) of fine-tuning approaches like PEFT/LoRA.
• Ensuring system reliability: When defining prompt contracts with structured output, constraints, and refusal rules for integration purposes.

Key capabilities

• Architecture Selection Guidance: Provides decision trees to choose between prompting, RAG, or fine-tuning based on needs.
• Model Scoring & Evaluation: Supports the creation of scoring matrices and automated evaluation pipelines (golden sets, A/B testing).
• Cost Optimization Strategies: Includes techniques like tiered models, caching, prompt caching, and budget guardrails.
• Fine-Tuning ROI Analysis: Offers tools to calculate break-even points and compare total cost of ownership for fine-tuning approaches.
• Prompt Contract Definition: Enables the creation of structured prompts with constraints (JSON schema, token limits).
• RAG Integration Patterns: Provides patterns for hybrid retrieval, reranking, packing, citing, and verifying information.

Example prompts

• "What LLM architecture should I choose for a new product requiring current knowledge?"
• "Can you show me how to calculate the ROI of fine-tuning an LLM with LoRA?"
• "How can I implement prompt contracts to ensure reliable output from my LLM application?"

Tips & gotchas

• Start Simple: Begin with simpler approaches like prompting before moving to more complex solutions such as RAG or fine-tuning.
• Cost Awareness: Focus on measuring cost per successful outcome, not just tokens used.
• Security First: Treat prompt injection and data leakage vulnerabilities as production code concerns.