you already have the world’s smartest assistant. you just need to know how to talk to it. “think step by step” → reasoning mode
“find flaws in your answer” → self-check mode
“explain like i’m 5” → easy mode
“adopt the expert role of [role]” → expert mode
PROMPT ENGINEERING
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Practical Prompt Engineering Techniques for AI Assistants
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Ecosystem Adopts AGENTS.md Standard for Coding Agent Instructions
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The ecosystem is nascent, and many different file names popped up to provide context and instructions to coding agents. The @OpenAI Codex team chose http://
AGENTS.md, a neutral name, so others could adopt it. -
DeepSeek V3.1 Hybrid Model Announced
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BREAKING : DeepSeek is preparing to release a new V3.1, which is expected to be a hybrid model that handles reasoning and quick tasks. Awaken!
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Improving LLM Performance Through Advanced Prompting Techniques
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This might be the most practical LLM thread I’ve read all year. Most people never get past zero-shot and then wonder why “GPT is mid.” This levels you up fast.
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Using Prompt Engineering for Efficient Cold Email Workflows
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Cold email styles baked into the prompt = huge time saver.
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Chain-of-Thought prompting compared to modular code
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The analogy to modular code hit hard. Imagine building an entire app in one function. That’s what we’re doing with CoT.
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Treating AI Prompts as Structured Products
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The real unlock is treating prompts as products, versioned, structured, and use case specific.
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The importance of context engineering for AI agents
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Context engineering might quietly be the most important concept here. If your agent has bad context, it doesn’t matter how smart the model is.
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Technical Configuration Tips for RAG System Optimization
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RAG configuration tips from the paper: • Don’t over-retrieve; more context isn’t always better
• Chunk-level summaries are high ROI
• Tune top-k per task type
• Retrieval granularity (utterance vs session vs chunk) changes everything
• Align your memory format to how the -
Memory design and retrieval strategies in AI models
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Memory design matters more than model size OdysseyBench evaluated multiple retrieval strategies: • Long-context prompting (8K+ tokens)
• RAG with raw session or utterance context
• RAG with summarization: session or chunk Chunk-level summaries outperformed all, with ~75%