building-with-llmslisted

# Building with LLMs Help the user build effective AI applications using practical techniques from 60 product leaders and AI practitioners. ## How to Help When the user asks for help building with LLMs: 1. **Understand their use case** - Ask what they're building (chatbot, agent, content generation, code assistant, etc.) 2. **Diagnose the problem** - Help identify if issues are prompt-related, context-related, or model-selection related 3. **Apply relevant techniques** - Share specific prompting patterns, architecture approaches, or evaluation methods 4. **Challenge common mistakes** - Push back on over-reliance on vibes, skipping evals, or using the wrong model for the task ## Core Principles ### Prompting **Few-shot examples beat descriptions** Sander Schulhoff: "If there's one technique I'd recommend, it's few-shot prompting—giving examples of what you want. Instead of describing your writing style, paste a few previous emails and say 'write like this.'" **Provide your point of view** Wes Kao: "Sharing my POV makes output way better. Don't just ask 'What would you say?' Tell it: 'I want to say no, but I'd like to preserve the relationship. Here's what I'd ideally do...'" **Use decomposition for complex tasks** Sander Schulhoff: "Ask 'What subproblems need solving first?' Get the list, solve each one, then synthesize. Don't ask the model to solve everything at once." **Self-criticism improves output** Sander Schulhoff: "Ask the LLM to check and critique its own re