Productivity AI tools: Text summarizers, tone polishers, and intelligent regex explainers.
Calculate API inference cost given prompt and completion token counts.
Structure complex prompts using Claude-compliant XML encapsulation tags.
Design sequential multi-step prompt chains (Extraction -> Verification -> Summarization).
Generate systematic Prompt Eval test case matrices in JSON format for automated benchmark suites.
Build high-accuracy Few-Shot prompt templates with structured Input/Output exemplars.
Scaffold step-by-step thinking directives ('Think step by step') to reduce reasoning errors.
Calculate total vector chunks and embedding costs given chunk size (512, 1024) and overlap (10%).
Explore greedy decoding vs creative temperature (0.0 to 1.5) and nucleus Top-P sampling.
Build OpenAI, Anthropic, and Gemini compliant function calling tool schemas.
Generate strict negative boundary prompt clauses ('Do NOT hallucinate', 'Never mention').
Generate tailored expert personas (Architect, Senior Reviewer, Security Auditor) for system prompts.
Generate strict JSON schema prompt constraints (response_format: json_object).
Allocate token budgets across System Prompt, RAG Context, Chat History, and Completion.
Generate defensive system prompt boundaries against DAN and prompt injection attacks.
Calculate cosine similarity ($0.00$ to $1.00$) between two numerical vector embeddings.
Configure stop token strings (e.g. 'Human:', 'Observation:', '```') to halt model generation.
Inject step-by-step reasoning triggers ('Think step by step') to increase LLM accuracy.
Compress system prompts by stripping filler words and whitespace to save 30% API costs.
Compare API costs across GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, and DeepSeek.
Format input/output demonstration pairs into clean few-shot LLM prompt templates.
Validate, fix missing quotes, and strip Markdown code blocks from LLM JSON responses.
Explore sampling temperature (0.0 to 1.5) and nucleus Top-P probability cutoffs.
Discover more utilities across different domains
