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6. Create Product Descriptions that Sell Prompt: "Write enticing product descriptions for our new line of products targeted at [Your Industry]. Each description should highlight the key benefits, ingredients, and what sets them apart from the competition."
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PROMPT ENGINEERING
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Prompt to create product descriptions that sell
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7 ChatGPT prompts that make you a superhuman
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7 ChatGPT prompts that'll turn you into a superhuman: (Bookmark this for later)
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Training LLMs: Avoiding Default Mode Through Data Curation
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First you’d teach the model quite a bit from recent content (w/ tons of LLM outputs in it it’s likely, if we were to train this in normally, the LLM will likely get stuck in that default LLM ‘mode’ we know so well + make it harder to break out of this w/ post-training) So
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Open Source Models Prompt Injection Security Flaw
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Yeah, I wouldn't trust that! That does highlight interesting flaw in a lot of open models though: I think there are some models that use strings like [INST] without even reserving a token for them, which opens up all sorts of additional potential prompt injection mischief
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Quick Prompt to Assess Model Capability Level
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Anyone got a good short go-to prompt for running against a model that instantly gives an indicator of how capable that model is? I'm trying to figure out a neat way of determining if a model feels gpt-3.5-turbo or gpt-4 level with the minimum amount of work (and API token spend)
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RAG and Token Optimization with Expanded Context Windows
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I was playing with maximizing RAG content last year, but that was when models still had tiny context lengths – 4,000 or 8,000 wasn't a lot to play with Token optimization like that is less interesting now we have 100,000+ tokens to play with even with the less expensive models
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LLM Randomness: Breaking Out to NumPy for Random Choice
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Whenever I want it to do the equivalent of random.choice, I break out to a tool that calls np.random. I'd love if I could just tell it to be random.
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Cost estimation and context optimization for advanced RAG queries
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I want to be able to give users a cost estimate before they run larger prompts, but I'm also interested in knowing how much content I can cram into the context for advanced RAG queries
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Anthropic Needs Public Tokenizer API Like Google Gemini
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Anthropic don't release any form of their tokenizer at all – not even an API endpoint that lets you count tokens before prompting with them (Google Gemini has that) It's a small thing but it's the feature I most want from them at the moment
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LLM Integration Challenges: IT vs. Educational Expertise
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No wonder IT departments are often baffled by working with LLMs (and teachers are often quite good at prompting them)