[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]  Rohan Paul [@rohanpaul_ai](/creator/twitter/rohanpaul_ai) on x 74K followers Created: 2025-07-13 19:53:24 UTC "hyper precise prompts to describe what you want", is absolutely the BEST strategy. 🔥 Many YCombinator AI startups prompts are super detailed (e.g. 6+ page prompts) with XML tags and meta-prompting techniques. e.g. Parahelp's customer support agent prompt is 6+ pages, meticulously outlining instructions for managing tool calls. --- ⚙️ Key Learning from this doc → Top AI startups use "manager-style" hyper-specific prompts—6+ pages detailing task, role, and constraints. These aren't quick hacks; they’re structured like onboarding docs for new hires. → Role prompting anchors the LLM’s tone and behavior. Clear persona = better alignment with task. Example: telling the LLM it's a customer support manager calibrates its output expectations. → Defining a task and laying out a plan helps break complex workflows into predictable steps. LLMs handle reasoning better when guided through each sub-task explicitly. → Structuring output using markdown or XML-style tags improves consistency. Parahelp, for instance, uses tags like <manager_verify> to enforce response format. → Meta-prompting means using LLMs to refine your own prompts. Feed it your prompt, outputs, and ask it to debug or improve—LLMs self-optimize well if given context. → Few-shot prompting with real examples boosts accuracy. Startups like Jazzberry feed challenging bug examples to shape LLM behavior. → Prompt folding lets one prompt trigger generation of deeper, more specific prompts. Helps manage workflows in multi-step AI agents. → Escape hatches instruct LLMs to admit uncertainty. Prevents hallucination and improves trust. → Thinking traces (model reasoning logs) and debug info expose the model’s internal logic. Essential for troubleshooting and iteration. → Evals (prompt test cases) are more valuable than prompts themselves. They help benchmark prompt reliability across edge cases. → Use big models for prompt crafting, then distill for production on smaller, cheaper models. Matches quality with efficiency.  XXXXXXX engagements  **Related Topics** [coins ai agents](/topic/coins-ai-agents) [coins ai](/topic/coins-ai) [Post Link](https://x.com/rohanpaul_ai/status/1944485332575039718)
[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]
Rohan Paul @rohanpaul_ai on x 74K followers
Created: 2025-07-13 19:53:24 UTC
"hyper precise prompts to describe what you want", is absolutely the BEST strategy.
🔥 Many YCombinator AI startups prompts are super detailed (e.g. 6+ page prompts) with XML tags and meta-prompting techniques.
e.g. Parahelp's customer support agent prompt is 6+ pages, meticulously outlining instructions for managing tool calls.
⚙️ Key Learning from this doc
→ Top AI startups use "manager-style" hyper-specific prompts—6+ pages detailing task, role, and constraints. These aren't quick hacks; they’re structured like onboarding docs for new hires.
→ Role prompting anchors the LLM’s tone and behavior. Clear persona = better alignment with task. Example: telling the LLM it's a customer support manager calibrates its output expectations.
→ Defining a task and laying out a plan helps break complex workflows into predictable steps. LLMs handle reasoning better when guided through each sub-task explicitly.
→ Structuring output using markdown or XML-style tags improves consistency. Parahelp, for instance, uses tags like
→ Meta-prompting means using LLMs to refine your own prompts. Feed it your prompt, outputs, and ask it to debug or improve—LLMs self-optimize well if given context.
→ Few-shot prompting with real examples boosts accuracy. Startups like Jazzberry feed challenging bug examples to shape LLM behavior.
→ Prompt folding lets one prompt trigger generation of deeper, more specific prompts. Helps manage workflows in multi-step AI agents.
→ Escape hatches instruct LLMs to admit uncertainty. Prevents hallucination and improves trust.
→ Thinking traces (model reasoning logs) and debug info expose the model’s internal logic. Essential for troubleshooting and iteration.
→ Evals (prompt test cases) are more valuable than prompts themselves. They help benchmark prompt reliability across edge cases.
→ Use big models for prompt crafting, then distill for production on smaller, cheaper models. Matches quality with efficiency.
XXXXXXX engagements
Related Topics coins ai agents coins ai