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GENERATIVE AI
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Mastering Prompt Engineering Fundamentals
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The meta-lesson from reverse-engineering Anthropic's library:
Prompt engineering isn't about clever tricks. It's about clear communication of: WHO should respond (role)
WHAT they should do (task)
HOW they should do it (process)
WHAT format to use (structure)
WHAT to avoid -
Prefilled responses: underrated prompt technique
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Tenth pattern: they use prefilled assistant responses. This is the most underrated technique in the entire library. Instead of just sending a prompt, Anthropic starts Claude's response.
API structure: { "messages": [ {"role": "user", "content": "Analyze this contract"}, -
Error Handling in AI Prompts
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Ninth discovery: error handling is built into prompts. Anthropic anticipates edge cases and tells Claude how to handle them. If the input data is:
– Incomplete: State what's missing and make reasonable assumptions
– Contradictory: Identify the contradiction and -
Negative examples in prompt engineering
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Sixth technique: constraint specification using negative examples.
Don't just say what you want. Say what you don't want. Standard approach: Write a professional email. Anthropic's method: Write a professional email that:
– Is concise (under 150 words)
– Has a -
Anthropic’s Thinking Tags for Complex Reasoning
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Fifth discovery: they use thinking tags for complex reasoning. When the task requires multi-step logic, Anthropic explicitly asks Claude to show its work. Before answering, wrap your reasoning in tags.
Include:
– Assumptions you're making
– Alternative -
Anthropic’s Secret Prompting Techniques Revealed
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Anthropic's internal prompting style is completely different from what most people teach. I spent 3 weeks analyzing their official prompt library, documentation, and API examples. Here's every secret I extracted
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Top AI Model Faces API Stability Issues
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I deleted the tweet because I posted it before it was released. And I still believe that since it is still the best model on the planet, so back to work you go, Alex, and make sure the API doesn’t crash every 3 calls.
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Grok AI Model Falls Short on Political Independence
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I would have thought that Grok is the most based model, i.e. most helpful in letting you think about edgy science stuff, most independent when discussing politics and policy etc., but it's not. Grok is a normie with an anime face
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Meta’s Continuous Chain-of-Thought Reasoning for LLMs
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Can reasoning LLMs think better if their Chain-of-Thought is continuous instead of discrete? This Meta paper introduces the first scalable way to train continuous CoTs with reinforcement learning—no need to distill from discrete references. By using "soft" tokens