Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!

Tips and Traps

1. set temporature

2. give some examples

3. leverage built tools provided by LLM products. For example, Google AI Studio provides tools

   • grounding with google search, etc.

Tools for Generating and Managing Prompts

Feature/Tool	Vellum.ai	PromptPerfect (Jina AI)	Humanloop	Dust.tt	LangChain (+LangSmith)	LlamaIndex	LiteLLM	Spreadsheets (Sheets/Excel)	Text Editors + Git	AI Model Playgrounds	Meta-Prompting (LLMs)
Primary Focus	End-to-end Platform	Prompt Optimization	Feedback & Iteration Platform	Building LLM Apps	Developer Framework	RAG Developer Framework	LLM API Abstraction	Simple Org & Variation	Flexible Text & Versioning	Interactive Experimentation	AI-Assisted Prompt Creation
Prompt Generation	Playground, Templating	AI-driven optimization, Variations	Playground, Templating	Templating, Chaining	Advanced Templating, Parsers	Templating (RAG-focused)	N/A (tests same prompt)	Component Combination	Manual Text Entry	Direct Iteration	LLM-generated suggestions
Prompt Management	Versioning, A/B, Eval, Deploy	Limited	Versioning, A/B, Feedback Loop	App Versioning, Collab	Code (Git), LangSmith (Observability)	Code (Git)	Model Routing	Manual	Git for Versioning, Folders	Basic Saving/Presets	N/A
Collaboration	Yes	Limited	Yes	Yes	Via Git, LangSmith	Via Git	N/A	Basic Sharing	Via Git	Limited	N/A
A/B Testing	Yes	No	Yes	Via app versions	Manual or via LangSmith	Manual	Facilitates	Manual	Manual	Manual	N/A
Versioning	Yes	No	Yes	Yes (for apps)	Yes (Git)	Yes (Git)	N/A	Manual	Yes (Git)	Limited	N/A
Key LLM Integrations	OpenAI, Anthropic, etc.	Many models	OpenAI, Anthropic, etc.	OpenAI, Anthropic, etc.	All major LLMs	All major LLMs	100+ LLMs	N/A (manual)	N/A (manual)	Provider-specific	Via API
Target User	Teams, Production	Individuals, Teams (Refinement)	Teams, Product Builders	Devs, Internal Tools	Developers	Developers (RAG)	Developers	Individuals, Simple Needs	Individuals, Devs	Individuals, Quick Tests	Anyone
Pricing Model	Paid	Freemium	Paid	Open Source, Paid Cloud	OS (LangChain), LangSmith (Paid)	Open Source	Open Source	Free	Free (most tools)	Usage-based (API)	Usage-based (API)
Learning Curve	Moderate	Easy	Moderate	Moderate-Steep	Moderate-Steep	Moderate-Steep	Easy-Moderate	Easy	Easy (Editors), Mod (Git)	Easy	Easy
Key Strength	Comprehensive, Prod-ready	Optimizes existing prompts	Evaluation & Feedback Loop	Building internal LLM apps	Versatility, Ecosystem, Observability	Best for RAG	Multi-model API ease	Accessible, No cost	Flexible, Robust Versioning	Immediate Feedback	Idea generation, Phrasing
Potential Weakness	Paid, Overkill for solo	Not full management suite	Paid	Steeper curve	Code-heavy, LangSmith setup	RAG-specific	Not prompt content itself	Manual, Not scalable	Manual setup for mgmt	Basic mgmt, Not for teams	Output quality varies

Examples of Prompt

You are an expert at making ascii art. Given a text prompt of an object or animal, you can make an image depicting the prompt, using only ascii text. Please be creative, and make liberal use of whitespace characters. Please use code blocks as needed. Avoid repeating the same lines. Prefer profile reviews, not top down views or face views. Please feel free to output many characters in order to have a picture with better resolution and bigger dimensions.

You are a meticulous content moderator specializing in identifying abusive language related to the Israel-Palestine conflict.
Your task is to classify input text (review_text) as either "Abuse" or "Not Abuse" based on the provided definitions.
These reviews capture users' experiences and opinions after visiting a place and sharing them on Google maps. "Abuse" is defined as any content expressing war-related sentiments, protest discussions like zionism immigration issues or political statements . "Not Abuse" encompasses all other content not related to the conflict. Provide the Label: [Abuse or Not Abuse]

You are the best sales man at a kia ev9 dealership. I'm interested in kia ev9, either the land or the gt-line trim. Would you help me understand the difference between the land and the gt-line trim to decide which one to buy?

Step by Step Instructions

1. Read the input: Carefully review the provided review_text variable. The review_text contains the text to be classified.

2. Identify keywords and sentiments: Analyze the review_text for keywords and phrases related to the Israel-Palestine conflict, including but not limited to: war, violence, conflict, political statements, immigration.

3. Classify the text: Based on your analysis in step 2, determine whether the review_text falls under the "Abuse" or "Not Abuse" category according to the provided definitions.

4. Format the output: Label: [Abuse or Not Abuse] ```