@hackproduct9: Love this topic. I’d make the caption educational rather than sarcastic, because it positions HackProduct as the place that teaches AI engineers to think about efficiency. Most people optimize for better prompts. Experienced AI engineers optimize for fewer tokens. Every unnecessary paragraph, repeated instruction, duplicated context, and oversized document costs: 💸 More money ⚡ More latency 📉 Lower throughput In production AI systems, token efficiency isn’t just about saving costs—it’s about building faster, more scalable applications. Some practical ways to reduce token burn: • Keep system prompts concise. • Retrieve only the relevant chunks (don’t dump the entire knowledge base). • Summarize conversation history instead of sending it every turn. • Cache reusable responses and embeddings. • Use the smallest model that can solve the task. • Let tools do the work instead of asking the LLM to reason through everything. The best AI engineers don’t just build prompts. They build systems that make every token count. Follow @hackproduct for practical AI engineering mental models. #AIEngineering #LLMs #PromptEngineering #RAG #GenAI

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Sunday 28 June 2026 04:04:22 GMT