Results for “prompt-caching”

Discovers and applies curated prompts from the prompts.chat collection to optimize AI interactions, prompt engineering, and workflow integration.

Guides production AI systems: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and caching.

Learn and apply prompt engineering techniques for LLMs, image generators, and video models using the inference.sh CLI.

Researches conversation and code context to generate 1-6 targeted clarifying questions when a prompt is vague, then executes the original request.

Analyzes draft prompts, identifies intent and gaps, matches ECC components, and outputs an optimized prompt for the user to paste and run. Advisory only, never executes the task.

Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates with evals.

Enhances plain-language requests into structured, reviewable prompts and inspects a self-hosted MCP gateway with provider-free defaults.

Analyze draft prompts to identify intent, scope, and missing context, then generate an optimized prompt with ECC component recommendations. Advisory only — never executes the task.

Generates copyable scientific figure prompts from research documents, papers, and reference images for AI illustration models.

Implements production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, advanced prompt engineering, cost estimation, quality evals, semantic caching, streaming, and agents.

Provides guidance and code for production AI workflows including RAG pipelines, vector databases, embedding indexing, prompt engineering, cost estimation, semantic caching, and quality evaluation.