llms-generative-ai
LLMs, prompt engineering, RAG systems, LangChain, and AI application development
prompt-design
Core prompt design patterns and templates for effective LLM communication
fine-tuning
LLM fine-tuning and prompt-tuning techniques
prompt-templates
Reusable prompt templates for common tasks and optimization patterns
ai-ml-technologies
Master AI, machine learning, LLMs, prompt engineering, and blockchain development. Use when building AI applications, working with LLMs, or developing smart contracts.
llm-jailbreaking
Advanced LLM jailbreaking techniques, safety mechanism bypass strategies, and constraint circumvention methods
prompt-engineering-patterns
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.
llm-evaluation
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
openai-prompt-engineer
Generate and improve prompts using best practices for OpenAI GPT-5 and other LLMs. Apply advanced techniques like chain-of-thought, few-shot prompting, and progressive disclosure.
anthropic-prompt-engineer
Master Anthropic's prompt engineering techniques to generate new prompts or improve existing ones using best practices for Claude AI models.
dspy-finetune-bootstrap
Fine-tune LLM weights using DSPy's BootstrapFinetune optimizer
dspy-gepa-reflective
Newest DSPy optimizer using LLM reflection on execution trajectories for agentic systems
llm-evaluation
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llm-gateway-routing
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multi-model-research
Orchestrate multiple frontier LLMs (Claude, GPT-5.1, Gemini 3.0 Pro, Perplexity Sonar, Grok 4.1) for comprehensive research using LLM Council pattern with peer review and synthesis
nlp-processing
Text processing, sentiment analysis, LLMs, and NLP frameworks. Use for text classification, named entity recognition, or language models.
lockfile
Use when creating llmring.lock file for new project (REQUIRED for all applications), configuring model aliases with semantic task-based names, managing environment-specific profiles (dev/staging/prod), or setting up fallback models - lockfile creation is mandatory first step, bundled lockfile is only for llmring tools
structured-output
Use when extracting structured data from LLMs, parsing JSON responses, or enforcing output schemas - unified JSON schema API works across OpenAI, Anthropic, Google, and Ollama with automatic validation and parsing
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