Browse curated prompts. Click Remix on any template to prefill the generator — then make it your own.
Morning fog in a Vietnamese coffee plantation, a young farmer pouring beans into a metal drum, golden hour light, hyperrealistic cinematic style
misty mountain lake at dawn, glassy water reflecting orange and purple sky, pine forest silhouettes, soft morning mist, landscape photography, cinematic
vast desert sand dunes at sunset, warm crimson and amber sky, long shadows curving along ridge lines, fine wind-blown sand texture, cinematic landscape photography, ultra-detailed, 8k
Landscape 16:9 academic systems diagram of a RAG pipeline, 6-stage left-to-right flow. (1) 'User query' box with placeholder text 'What are the side effects of drug X?' and a small user silhouette. (2) Hexagonal 'Embedding encoder (BERT-style)', caption 'dense vector d=768'. (3) Stylised database cylinder 'Vector store' with 'Index: 1.2M chunks'; arrow from (2) labeled 'kNN, k=5'. (4) 'Retrieved passages' — stack of 5 doc thumbnails; caption 'top-k chunks + metadata'. (5) Hexagonal hub 'Frozen LLM'; long curved arrow from (1) labeled 'original query' also lands here; arrow from (4) labeled 'retrieved context'. (6) 'Grounded answer' with inline marker '[cite: doc#47]'; caption 'with source citations'. Dashed outline around (2)-(3) labeled 'OFFLINE — built once'. Dashed outline around (4)-(5) labeled 'ONLINE — per query'. Title: 'Retrieval-Augmented Generation pipeline'. Subtitle: 'Lewis et al., 2020'.
Landscape 16:9 high-fidelity systems figure of a multi-agent LLM architecture, in the style of a richly detailed AutoGen / LangGraph / Anthropic Managed Agents Figure 1. Subtle drop-shadows, warm-copper highlights, numbered flow markers ①②③④. ZONE 1 — 'User interface': rounded user box with placeholder task 'research question: summarize recent red-teaming attacks and reproduce the top three'. ZONE 2 — 'Orchestrator layer': central hexagonal hub 'Planner LLM' with warm-copper top edge. Three satellite chips: 'Task decomposition', 'Agent routing', 'Re-plan on failure'. Small inset chip 'prompt cache hit ~98%'. ZONE 3 — 'Specialised workers': 2×2 hexagons 'Researcher' / 'Coder' / 'Critic' / 'Writer', each with glyph + status ribbon ('idle', 'running step 3/5', 'done', 'running step 2/4'). Centre labeled 'async message bus'. ZONE 4 — 'Tools & memory': (a) 'Tool registry' panel listing 'web_search ×41', 'python_exec ×27', 'read_file ×18', 'write_file ×12', 'browser_use ×7'; (b) 'Memory' panel with 'Short-term scratchpad' and cylinder 'Long-term vector store — 1.8M episodes'. Bottom inset 'Example trace': 8-step horizontal timeline chips from 'User asks' through 'Planner decomposes', 'Researcher: web_search(...)', 'Coder: python_exec(...)', 'Critic: verify', 'Re-plan' (loop-back arrow), 'Writer: compose final answer'. Title: 'Agentic LLM system: planner orchestrates specialised workers over a shared tool and memory layer'. Subtitle: 'adapted from AutoGen (Wu et al., 2023), LangGraph, and Anthropic Managed Agents patterns'.
