7/ Phase 3: Design Every API call costs money. More users = more burn. Design around:
• Prompt efficiency
• Model tiering
• Smart caching
• Product patterns (Copilot, Agent, Augmentation)
PROMPT ENGINEERING
-
Phase 3 Design: Prompt Efficiency, Model Tiering, Smart Caching, Patterns
By
–
-
Prompt Engineers: The New Creative Directors
By
–
Prompt engineers are becoming the new creative directors.
-
Claude made simple: free guide with mini-course and prompts
By
–
Claude made simple: grab my free guide → Learn fast with mini-course
→ 10+ prompts included
→ Practical use cases Start here ↓ -
Prompting Power: Skip Consultants, Build Closer, Move Faster with LLMs, Data, Questions
By
–
If you know how to prompt, you can: – Skip the consultants
– Build closer to your customer
– Move 10x faster The new market research stack = → LLMs
→ Data
→ Clear questions Prompt smarter. Build better. -
Prompt to generate 3 distinct and realistic customer personas
By
–
Prompt to generate personas:
— God of Prompt (@godofprompt) 9 septembre 2025
“You are a senior market researcher and customer insights strategist trained in persona development, behavioral segmentation, and product marketing.
I want you to create 3 distinct and realistic customer personas based on the following product or… pic.twitter.com/FssjUhNmct—
Prompt to generate personas: “You are a senior market researcher and customer insights strategist trained in persona development, behavioral segmentation, and product marketing. I want you to create 3 distinct and realistic customer personas based on the following product or -
Concatenative Languages and RL Environment Limitations
By
–
RQ6: Where best apply a concatenative language like Joy? Most datasets are rather static! Even many "RL" environments are single-turn, simple wrappers around static datasets that only allow the model space to think about a unique answer. The authors of the paper I linked
-
Free System Prompt Generator for ChatGPT, Claude, Gemini, DeepSeek
By
–
I should charge $200 for this. But I'm giving it away for free. It's a System Prompt Generator that builds expert-level agents for: → ChatGPT
→ Claude
→ Gemini
→ DeepSeek Like + comment “Agent” and I’ll DM you the file. (Must follow) -
Tool calling, context engineering, RAG, and fact-checking help truth.
By
–
7/ So what does help? Tool calling. Context engineering. Retrieval-augmented generation (RAG). External fact-checking. If you care about truth, offloading to real tools and live sources is your best shot.
-
How prompts add caution, uncertainty bias, and unverified tags
By
–
5/ So what’s really happening? • Prompt adds cautious words to the context vector
• Model predicts next token with a bias toward uncertainty
• It still pulls from patterns of fake info in the training data
• And slaps an [unverified] tag on top -
Models add disclaimers instead of stopping hallucinations, a style issue.
By
–
3/ Example: A prompt says: "Never present generated content as fact." The model doesn’t stop hallucinating. It just adds: “I cannot verify this information” – even if it’s entirely fake. This isn't honesty. It’s style.