My couch. MacBook Pro. Runpod for compute. Axolotl for training. OpenAI Playground and Anthropic Console for prompt engineering. Sometimes PromptKnit.
PROMPT ENGINEERING
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Complete AI Development Stack: Runpod, Axolotl, and Prompt Engineering
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My couch. MacBook Pro. Runpod for compute. Axolotl for training. OpenAI Playground and Anthropic Console for prompt engineering. Sometimes PromptKnit.
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Adding Example Inputs Outputs Improves Prompt Performance
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Try adding example inputs/outputs to the prompt!
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API Optimization and qLoRA Fine-tuning Techniques
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On APIs, it’s hard but if you’re going after a specific use-case it just takes a bit of tinkering with the prompt. If not, just do a qLoRA with a couple hundred negative examples and you’re all set.
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Prompt First: When to Fine-tune AI Models
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Always, always try prompting first. The only time you should fine-tune is when you NEED to.
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LLM API Strategy: Wait for PMF Before Fine-tuning
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If the LLM API can do your task, if you can afford the costs, wait to fine-tune until you have PMF and need to optimize.
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Chaining Smaller Prompts vs Mega Prompts Strategy
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Usually stringing smaller prompt outputs together with code is better, but mega prompts are a close and easier second.
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Good Prompts Prevent AI Model Failures Without Additional Safeguards
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I’ve never tried it or had a need to. If you write good prompts, you don’t need to worry about the model going off the rails.
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Fine-tuning Open-ended LLM Agents: Challenges and Personality Experiments
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The most fun, but frustrating to do (due to data challenges) are open-ended agent fine-tunes. Easier ones are more chat/tool-use focused. A really fun one was experimenting with changing a LLM’s personality significantly. Made it “sassy” lol
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Pre-training vs Fine-tuning: Learning Strategies in AI Models
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When you’re pre-training, you basically learn from everything in the dataset. When fine-tuning, you often only want the model to learn from the text in the output, not the prompt itself.