PROMPT ENGINEERING

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5 ChatGPT Prompts for Students to Boost Studying

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@godofprompt

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20 April 2024 12h49

I'm shocked why most students don't use ChatGPT for studying. Here are 5 ChatGPT Prompts for students to know about:

→ View original post on X — @aibreakfast

GPT-5 Predictions: Multi-modal generation from any input with precise refinement

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@aibreakfast

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20 April 2024 7h18

Alright, GPT-5 Predictions: Generate text, images, video, 3D assets or music FROM text, images, video, or music (basically it can take anything as input, and produce any medium for output depending on the prompt direction) with much more precise refinement process to massage an

Handling Chatbot Self-Help Queries with RAG and Prompting

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20 April 2024 2h47

If you ship a chatbot, it's now indisputable that people are going to ask it questions about how to use it Handling this isn't trivial but it's not unsolvable either – use RAG, fine-tuning or a meticulous system prompt, it's important to anticipate this use-case

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Flow Engineering Webinar Recording with LangChain Leaders

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@langchain

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20 April 2024 1h37

The recording from our Flow Engineering webinar with @hwchase17 and @itamar_mar is up! https://
youtube.com/watch?v=eBjxz7
qrNBs
… "Flow Engineering" is a term that has been gaining in popularity recently. The first time it was mentioned as term was in CodiumAI paper on AlphaCodium, where they

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Stable Diffusion 3 Now Available on Poe Platform

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@poe_platform

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19 April 2024 23h25

New: Stable Diffusion 3 is on Poe! SD3 is the latest image generation model from Stability, and it is especially good at typography and prompt accuracy. It understands complex prompts, supports multiple languages, and has improved spelling over SDXL. (1/2)

Fine-tuning GPT-3.5: Use Cases and Community Experiences

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19 April 2024 21h10

Using https://
platform.openai.com/docs/guides/fi
ne-tuning
… ? What have you used that for – I've not heard many examples of people fine-tuning 3.5

Fine-tuning vs RAG: When to Use Each Approach

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19 April 2024 18h48

That's not the same as believing that fine tuning can't teach a model new knowledge under any circumstances – but in practice many people I see who think they need to fine tune a model would be better off using RAG instead

RAG vs Fine-tuning for Small Document Loading

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19 April 2024 18h47

My understanding is that if you want to eg load your company employee handbook (~20 page PDF) into a model you're likely to get much better results with RAG than with fine-tuning – can you fine tune such that the model reliable answers questions about something that small?