mark-wiemer

sequenceDiagram
actor User
    User->>Copilot: What's the weather like today?
    participant LM as Language model<br />(Gurbo)
    Copilot->>LM: Pick a plugin from the following list for this prompt:<br />Prompt: "What's the weather like today?"<br />Plugins:<br />1. Sports: get info about sporting events<br />2. Weather: get the current weather for a region<br />3. News: get recent news on a topic<br />Just say the plugin name, nothing else
    LM->>Copilot: Weather
    Copilot->>Weather plugin: Get current weather for <device location>
    Weather plugin->>Copilot: { temp: 70, unit: "F", sky: "sunny" }
 Copilot-&gt;&gt;LM: Summarize this weather: '{ temp: 70, unit: "F", sky: "sunny" }'Be professional and friendly

mark-wiemer

HuggingGPT paper, figure 4, part 1

From HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace, transcribed by Mark Wiemer. All efforts were made to keep text as written. Emojis have been used to replace icons in the response.

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Query: generate a video with the title "An astronaut is walking in space" and dub it.

Response: Sure. I have generated a video with the title 'An astronaut is walking in space' and dubbed it. The workflow I used is as follows: First, I used the text-to-video model ' 🤗 damo-vilab/text-to-video-ms-1.7b' to generate a video from the text. The inference result is the generated video ' 📽️ /videos/5696.mp4'. Second, I used the text-to-speech model ' 🤗 facebook/tts_transformer-es-css10' to generate an audio from the text. The inference result is the generated audio ' 🎵 /audios/3f9e.flac '. Finally, I combined the video and audio to generate the dubbed video. I hope this

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mark-wiemer

Conversations with the new Bing

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Define prompt engineering in 20 words, and please provide three brief examples.

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mark-wiemer

Prompt engineering article (ChatGPT Mar 23 Version, GPT-3.5)

Define prompt engineering

Prompt engineering is the process of designing and constructing high-quality prompts or cues for language models or other artificial intelligence (AI) systems. The goal of prompt engineering is to create prompts that can effectively guide the AI system to generate the desired output.

Prompt engineering involves selecting the appropriate input format and designing the prompts in a way that they provide enough context to the AI system to generate the desired output. The prompts need to be carefully crafted to ensure that they are both informative and specific enough to guide the AI system, while also being general enough to allow for flexibility in the output.

Effective prompt engineering is crucial for achieving the desired performance from AI models, such as generating coherent and accurate text responses, improving model accuracy, and reducing the likelihood of generating unintended or biased responses. It requires