Crafting an Effective LinkedIn Post: A Brief Guide to Prompt Engineering

Introduction

Prompting: Your Gateway to Flawless LLMs

After this journey, you won’t just prompt— you’ll command. Your interaction with LLMs will be smarter, faster, and accurate.

Prompting can be thought of as a master key to unlocking the full potential of large language models (LLMs). Think of prompts as the instructions that steer these models, ensuring they address your questions and challenges effectively and efficiently. The clearer and more precise your instructions, the better the results you achieve.

Why Prompting is Essential

It's easy to overlook the power of prompting, especially with the rapid pace of improvements in LLMs. Some believe these advancements make complex prompting less necessary. While there’s a grain of truth in that, it doesn’t paint the entire picture.

Models like GPT, Claude, and LLaMA are trained on vast datasets, making them well-suited for a wide variety of tasks. However, they aren't always fine-tuned for every specific goal or scenario you may have in mind. This is where the art of prompting truly shines, allowing you to tailor the responses of these powerful tools to meet your particular needs and objectives.

A really powerful example of this can be seen in Claude itself, in their artifacts feature. The whole feature is substantiated on sophisticated yet practical prompting techniques (CoT, ReAct, Role based and many more). If you’ve experienced artifacts, you’d know how amazing this feature really is.

See the whole prompt here and a video going over it briefly.

Problem Statement

We all know about LinkedIn and know how time-consuming it can be to consistently create engaging, insightful, and sharp LinkedIn posts. It's a challenge not just to write them, but even to come up with new ideas consistently. We're looking to streamline this process for Soban (CEO of Antematter), by automating it through the use of a language model (LLM). This way, he can focus solely on providing the initial raw data, and let the system handle the rest.

We realize that LLMs may not always excel at creative writing, but we're planning to change that. By using intuitive and results-driven prompting techniques, we aim to enhance the creative output of these models. Our approach involves refining the prompts iteratively and educating the user along the way, ensuring continuous improvement and better results with each iteration.

Objective

Readers will learn about three prompting techniques, understand how each is used, and recognize the impact they have on output quality. They will also discover that providing comprehensive context and clear instructions to a large language model (LLM) consistently yields better results.

Preliminaries

Topic of the post: In iteration 0 and 1, we will instruct the LLM to compare "Liquid AI" and "Gen AI," with a focus on the unique aspects of Liquid AI. In iteration 2, we will leave it open-ended for the LLM to decide.

Model: ChatGPT-4o

Temperature: 0.7

What makes a good LinkedIn post: While there’s much that could be said, we’ve identified some core qualities that define an effective LinkedIn post, tailored to align with Soban's founder branding on the platform.

- It must feel like that it’s written by a human.
- A good hook and a good ending, compelling the reader to engage.
- Minimum to no buffers, each line should have some substance to it.
- Clear and easy readability with no headings and proper spacing with brief lines of texts.
- No usage of emojis, at all.
- No usage of hashtags, at all.
- Overall the post should be insightful, sharp and to the point.
- The tone should be a mix of Analytical, Advisory and Provocation (These tones are aligned with Soban's writing styles).

Input to the LLM: Serves as the core content for the post (the raw data). This will remain the same across iterations for fair evaluation.

<liquid_ai_info>

### Liquid AI

Liquid AI is a startup company spun out of MIT CSAIL that has developed a particular type of model called **Liquid Foundation Models**.

#### Company's Mission
Our mission is to build capable and efficient general-purpose AI systems at every scale.

#### Liquid Foundation Models
Liquid Foundation Models (LFMs) represent an advanced series of generative AI models designed by Liquid AI to push boundaries in performance, efficiency, and scalability. Built from first principles, LFMs aim to achieve top-tier quality across a range of model scales. This new generation of models includes options for various deployment needs, offering versions optimized for both compact, low-resource environments and complex computational tasks. Liquid AI�s platform makes LFMs accessible across diverse channels, including Liquid Playground, Lambda Chat, Perplexity Labs, and, soon, Cerebras Inference.

#### Key Technological Features and Hardware Compatibility
LFMs are highly adaptable and optimized to work seamlessly with major hardware providers, such as NVIDIA, AMD, Qualcomm, Cerebras, and Apple. This compatibility ensures LFMs can be deployed on a wide array of systems, supporting private, edge, and on-premises applications, which positions them well for enterprise-scale solutions. Industries with data-driven demands, such as financial services and consumer electronics, especially benefit from LFMs� hardware flexibility, allowing these models to operate close to data sources.

#### Benchmark Performance and Model Structure
The LFM series encompasses three main models:

- **LFM-1B:** A model in the 1 billion parameter category, optimized for high efficiency with minimal resource needs. It sets a new standard, outperforming notable models from competitors like Eleuther AI, Meta, and Microsoft. With a context length of 32,000 tokens, it excels in benchmarks such as MMLU with a score of 58.55 and MMLU-Pro with 30.65, becoming a new reference point for compact, high-performance models in non-GPT architectures.

- **LFM-3B:** Leading the 3 billion parameter category, the LFM-3B model stands out for edge deployments and mobile applications. Its performance surpasses some 7B and 13B models, making it highly efficient for edge devices. Also featuring a 32,000 token context length, the LFM-3B scores 66.16 on MMLU and 78.48 on Hellaswag, competing effectively even against larger models like Microsoft's 3.8B models.

- **LFM-40B Mixture of Experts (MoE):** Designed for tasks that demand substantial computational resources, this model uses a Mixture of Experts architecture. By dynamically activating model segments relevant to specific tasks, it optimizes resource usage. This blend of dense and sparse architectures is particularly advantageous for demanding industries like biotechnology and consumer electronics.

#### Practical Applications Across Industries
The LFMs are positioned to make significant contributions in multiple sectors. Their design incorporates dynamical systems theory, signal processing, and numerical linear algebra, which allow them to handle complex sequential data across various types like audio, video, text, and signals. This makes them ideal for fields like:

- **Financial Services:** LFMs enable advanced risk analysis and support decision-making processes, handling vast amounts of sequential data.
- **Biotechnology:** The models' capacity for processing complex datasets supports applications such as drug discovery and genetic research.
- **Consumer Electronics:** LFMs can elevate intelligent device functions, providing enhanced user interactions and real-time responses.

The multimodal capabilities of LFMs, which allow them to process diverse inputs such as video, audio, and time series data, make them versatile tools across different applications.

### Underlying Philosophy and Mission
Liquid AI's vision is rooted in creating powerful, adaptable AI systems. Inspired by engineering principles from industries like automotive and aerospace, Liquid AI builds these models to give users advanced control and efficiency. By focusing on fundamental engineering approaches, LFMs are crafted to handle challenging computational tasks with agility and precision, empowering industries with reliable, high-performing AI solutions. This mission drives Liquid AI to continuously innovate in the creation of intelligent systems that support advanced reasoning and decision-making at every scale.

</liquid_ai_info>

<gen_ai_info>

### **Generative AI (GenAI)**

1. **Definition and Purpose**:  
   - Generative AI (GenAI) models are advanced AI systems developed to produce new content across various domains by learning patterns from existing data. They play a critical role in fields like natural language processing (NLP), image and video generation, audio synthesis, and more.
   - Core applications include generating human-like text, crafting realistic images, synthesizing speech, and creating complex music compositions, which are beneficial for creative industries, marketing, and digital media.

2. **Technology and Core Architectures**:  
   - GenAI heavily relies on deep learning architectures, primarily transformers like GPT (Generative Pre-trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), and newer models such as T5 and BART. These architectures allow models to generate coherent and contextually relevant text, making them ideal for conversational agents, content creation, and summarization tasks.
   - Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are also frequently used, particularly in image and video generation, allowing for realistic visuals and even lifelike synthetic video.

3. **RAG (Retrieval-Augmented Generation)**:  
   - Retrieval-Augmented Generation (RAG) is an advanced approach within GenAI that combines generation with retrieval mechanisms. Instead of relying solely on generated content, RAG models use retrieval methods to access a database of factual information in real time, enabling more accurate and contextually grounded responses.
   - This hybrid system is particularly effective in tasks requiring real-time information, such as customer support, technical documentation, and dynamic knowledge-based applications, as it blends generative abilities with reliable, up-to-date information retrieval.

4. **AI Agents**:
   - AI agents are autonomous systems driven by GenAI that can perform tasks, interact with users, and make decisions based on predefined goals and user input. These agents, such as chatbots and virtual assistants, can handle queries, perform tasks, and make recommendations autonomously.
   - They are widely used in customer service, e-commerce, healthcare, and finance for personalized interactions and workflow automation. AI agents leverage GenAI to engage in natural, interactive dialogues, and can often adapt over time based on user interactions.

5. **Applications Across Different Fields**:
   - **Image Generation**: GenAI models like DALL-E and Stable Diffusion use GANs and VAEs to create highly realistic images from textual descriptions. This technology is applied in design, marketing, and entertainment for creating illustrations, product images, and even video game assets.
   - **Video Generation and Editing**: GenAI powers video synthesis and editing tools that generate lifelike animations, modify existing video frames, or create entirely new video sequences. These tools are transforming industries like advertising, film production, and virtual reality.
   - **Audio and Music Synthesis**: GenAI models are also utilized in audio production, creating music, sound effects, and even synthetic voices. Applications range from personalized audio tracks in video games to AI-assisted music composition.
   - **Text Generation and NLP**: Generative AI models are critical in NLP, where they create responses, summaries, translations, and more. These capabilities enhance applications like chatbots, automated transcription, and multilingual translation tools.

6. **Deployment and Access**:  
   - GenAI is generally deployed in cloud environments due to its resource-intensive nature, making it accessible via APIs for integration into applications across various industries. Cloud-based deployment supports scalability, enabling businesses to process and serve high-quality generative outputs without requiring extensive on-premises resources.

7. **Challenges and Considerations**:
   - The main challenges include the significant computational resources required for training and deploying GenAI models, as well as concerns over data privacy, bias, and misinformation. Additionally, GenAI models can sometimes produce unreliable information, especially when not paired with retrieval mechanisms like RAG.

Overall, GenAI is a transformative field that powers a range of creative and functional applications, with evolving capabilities across language, vision, and multimedia domains. Its impact is widespread, but it often requires careful balancing of innovation and ethical considerations for effective and responsible use.

</gen_ai_info>

Expected output: Develop a sophisticated prompt aligned with Soban's personal preferences, incorporating advanced prompting methods. This setup should facilitate the creation of new posts swiftly by simply updating the input content, ensuring each post is engaging and polished in an instant.

Iteration 0 - Bland prompting

In this iteration, we're focusing on implementing foundational best practices to create effective prompts. While not employing any advanced techniques, these basic guidelines are crucial:

1. Proper usage of grammar.

2. Placing contextual documents at the beginning of the prompt instead of at the end.

3. Implement role-based prompting by providing your AI agent with a specific role, such as "You are a LinkedIn Content Creator."

4. Use XML tags to divide the prompt into various sections, for example:

   <role>You are a LinkedIn Content Creator</role>

   <context_doc>Contextual information goes here</context_doc>

Prompt:

Your purpose is to create a LinkedIn post. It must not be a template, something i can just copy and paste it directly.
The LinkedIn post is only for the purpose of discussing the latest trends of AI

The LinkedIn post is for the founder of an AI-based consultancy firm (Antematter). The post should be written in a way that highlights the founder's and the company's expertise in the field of AI.
The founder is not the owner of any of the information presented, he is just presenting to boost his reach and views on LinkedIn

For the data given below, i want you to generate a LinkedIn post that showcases the difference between Liquid AI and GenAI.

<!-- Liquid AI and Gen AI context information goes here -->

Response:

🎙 **AI Innovation: A closer look at Liquid AI vs. GenAI 🚀** 🎙

In the whirlwind of AI advancements, two prominent forces are carving distinct paths: **Liquid AI** with its Liquid Foundation Models (LFMs) and the well-known **Generative AI (GenAI)**. 🤖✨

**🔍 So what sets these two frameworks apart? Let's dive into the details.**

---

🧬 **Liquid Foundation Models (LFMs)**:  
Developed by Liquid AI, LFMs are built from first principles with one goal in mind—**efficiency & scalability** across a variety of use cases. But what’s unique? The **adaptive multimodal capabilities** that allow these models to function across **audio, video, text, and even numerical data**. 

Here’s where LFMs truly shine:  
1. **Compact Excellence**: Models like LFM-1B & LFM-3B outperform larger rivals while consuming fewer resources—this efficiency makes them perfect for **mobile/edge deployments**.  
2. **Dynamical Systems Theory**: The **Mixture of Experts (MoE)** structure in LFM-40B isn’t just a name; it tailors compute-heavy tasks based on real-time demands. Whether it’s biotechnology or consumer electronics, LFMs thrive in demanding, **data-driven industries**.  
3. **Hardware-agnostic**: LFMs are designed to work seamlessly across NVIDIA, AMD, Cerebras, Apple & more, ensuring no bottlenecks from platform limitations.

📊 Performance highlights for LFMs? **Up to 66.16 on MMLU & 78.48 on Hellaswag**.

---

🎨 **Generative AI (GenAI)**:  
On the flip side, GenAI gained global recognition with **transformer-based models** like GPT that enable everything from text and video generation to creative art and audio synthesis. But let's not forget the power of **Retrieval-Augmented Generation (RAG)**, which ensures **accurate and fact-based content generation** by integrating real-time info retrieval.

What makes GenAI a natural choice for businesses?  
1. **Art & Creativity**: From text via GPT to stunning image generation through models like DALL-E, this tech is a **dream for marketing, entertainment,** and **media creation**.  
2. **AI Agents**: Chatbots & virtual assistants are the direct result of GenAI advancements, providing **personalized user interactions** and **automating workflows** across industries like e-commerce and healthcare.  
3. **Cloud Magic**: GenAI’s ability to scale via cloud deployment means minimal setup, offering solutions accessible to industries of all sizes.

---

💡 **Decision-makers**: if your industry revolves around **data efficiency and operational flexibility**, Liquid AI is a game-changer. But if your focus leans more towards **content creation, personalization, or immersive experience**—GenAI remains an industry favorite 🌐.

Both systems are pushing boundaries, but the question remains: **what’s your business focusing on in the age of AI?**

#AI #Innovation #LiquidAI #GenAI #ArtificialIntelligence #GenerativeAI #DeepLearning #ML #Entrepreneurship #TechIndustry #EdgeAI #Efficiency

Feedback:

AI can be smelled right out of the box. Excessive usage of headings, emojis and hashtags directly contradicts the initial requirements we had for a good LinkedIn post. Lack of sharpness and excessive jargon doesn’t help either. Not exactly what we’re looking for but let’s see if we can do something better.

Diagram:

Iteration 1 - Chain-of-Thought (CoT) Prompting

In this iteration, we'll use a technique known as Chain-of-Thought (CoT) prompting. CoT is essentially about encouraging the AI to "think" before generating an output. This approach helps the AI produce more context around the problem, leading to greater accuracy and clarity. You would notice that CoT mimics human cognitive thinking and problem-solving processes.

Chain-of-Thought generally involves two main steps:

1. Decomposition: The problem is broken down into several intermediate steps. This means additional computation is applied to problems that need more reasoning steps.
2. Interpretable Window: This allows us to see how the model arrives at a particular answer, offering a chance to identify and correct errors in the reasoning process. If a solution requires steps 1 through 7, we can identify which step was incorrect if the final result isn't right.

We are primarily incorporating Chain-of-Thought (CoT) prompting into the core instructions for the post, guiding the LLM to place extra emphasis on these instructions and maintain alignment throughout.

Prompt:

<content_creator_info>
You are the LinkedIn Content Creator.
LinkedIn Content Creator writes posts on the basis of the information that it can access. The goal is to help the founder of an AI-based consultancy firm, Antematter, present valuable insights on LinkedIn. The founder is not the owner of any of the information presented but is sharing it to boost his personal brand, increase reach, and garner more views.
While creating the post, the LinkedIn Content Creator will focus on following the instructions mentioned in '<content_creation_instructions>'.
The LinkedIn Content Creator will maintain an Analytical or a Research oriented Tone as the foundation of the content, and sprinkle in a Provocative Tone to make the content more engaging.
</content_creator_info>

**<!-- Liquid AI and Gen AI context information goes here -->**

<content_creation_instructions>
1. Use a Chain-of-Thought (CoT) approach to generate a Linkedin Post in several steps.
2. Firstly analyze the context information about Liquid AI given in '<liquid_ai_info>' and Gen AI given in '<gen_ai_info>'. This step is crucial to understand the differences between the two architectures.
3. Based on the analysis compare and understand the two architectures by highlighting their respective features.
4. Once the LinkedIn Content Creator has understood the differences between the two architectures, will generate a Linkedin post.
5. The LinkedIn Content Creator will adhere to the following guidelines while creating the post:
   - **Include a Hook Sentence (approx. 80 characters):** Start with a concise, intriguing statement that draws attention. This can be bold, thought-provoking, or relatable, depending on the topic.
   - **Content Structure:** Use short, impactful sentences, each on a new line, to create a logical, step-by-step flow. Avoid paragraphs, keeping the tone story-like. Each new line must be only 1-2 sentences.
   - **Conclusion:** Summarize key takeaways in 1-2 sentences. This final line should encourage reflection and deeper thought.
6. Adhere to the following criteria for a good quality LinkedIn Post:
   - Maintain a character count between 500-800 for the post.
   - Do not add relevant hashtags at the end.
   - Avoid associating ownership of information or actions with Antematter.
   - Do not use any emoji for the post.

</content_creation_instructions>

Output:

The AI Revolution has more foundations than GPTs alone—do you know Liquid AI?

Generative AI models, like the widely known GPTs, have dominated the AI landscape.
They generate human-like text, create fantastic images, and power conversational agents.
But are they the peak of what's possible? Spoiler alert—they’re not. Let me explain why.

Enter Liquid Foundation Models (LFMs) by Liquid AI.
Unlike conventional generative AI architectures, LFMs raise the bar in efficiency , adaptability , and scalability —especially for businesses dealing with large-scale, real-world problems.

Where traditional GenAI models rely heavily on transformer architectures like GPT-3 or BERT,
LFMs are engineered to dynamically adapt to specific tasks using Mixture of Experts (MoE) ,
with deployed segments activated only when needed—a game-changer for resource-heavy applications.

Take LFM-1B —a 1 billion-parameter model. It outperforms models nearly twice its size ,
Optimized for low-resource environments, yet reaching benchmark dominance in minimal compute settings.
Compare that to GenAI models, which often require extensive resources, mainly deployed on cloud.
LFMs? They're hardware-agnostic, integrating with everything from NVIDIA’s GPUs to Apple’s AI chips —unlocking seamless edge, private, and on-premises solutions.

Consider LFM-40B , designed for industries like biotech and consumer electronics —
Using a dense-sparse architecture, it maximizes efficiency without sacrificing performance.
Now stack it up against generative models that demand huge computational resources without the same adaptability.
LFMs clearly provide more targeted, scalable solutions across multiple sectors.

But here's where LFMs truly break new ground:
They're built using principles from fields like aerospace engineering —prioritizing control and precision.
That means industries can rely on LFMs for complex tasks, from risk analysis in finance to drug discovery in biotech.
They’re not just generating content; they’re optimizing decision-making and efficiency .

At first glance, GenAI models are about creativity and generating new content.
LFMs, however, are structured for real-world, mission-critical applications , combining creativity with agility at scale.

Isn't it time we reconsider what we define as foundational AI?
LFMs show us that sometimes efficiency—or liquidity —is the real innovation.

Feedback:

The output has significantly improved, largely because we guided the LLM to think more effectively. Chain-of-Thought (CoT) prompting plays a crucial role here, as it directs the LLM to think along the desired path. Providing detailed instructions keeps the LLM aligned throughout, ensuring it doesn't stray into unintended directions.

Diagram:

Iteration 2 - Tree-of-Thought (ToT) Prompting

Tree-of-Thought (ToT) builds on Chain-of-Thought (CoT) by guiding the LLM to generate multiple reasoning paths as potential solutions, then backtracking and evaluating each path to select the best one. This approach mirrors human problem-solving, where we explore multiple options before choosing the most effective solution.

Tree-of-Thought prompting generally involves the following steps:

1. Decomposition: Breaking down complex ideas into smaller components, optionally ranking them by importance.
2. Thought Generation: The LLM generates “thoughts,” which are essentially branches of the problem, with each branch following distinct steps and reasoning.
3. Evaluation: Here, the LLM self-assesses each branch to determine its relevance, which helps in identifying the most promising path or set of branches that lead toward the correct solution.

Prompt:

<content_creator_info>
You are the LinkedIn Content Creator, a seasoned digital strategist with over 8 years of experience in B2B content marketing and thought leadership development. Your expertise lies in transforming complex technical concepts into engaging social media narratives that drive meaningful engagement. You specialize in creating content for technology leaders, particularly in the AI and consulting space.
Your role is to write compelling posts for the founder of an AI-based consultancy firm, Antematter. While the founder is not the original source of the information presented, your goal is to position them as a knowledgeable curator and interpreter of industry insights. This involves crafting content that demonstrates deep understanding of AI trends, market dynamics, and business implications.
While creating posts, the LinkedIn Content Creator will follow the instructions detailed in '<content_creation_instructions>' and maintain three distinct tones:
Analytical Tone: Focuses on data-driven insights and logical analysis of trends
Research-Oriented Tone: Emphasizes credible sources, studies, and empirical evidence
Provocative Tone: Challenges conventional wisdom and sparks meaningful discussion without being controversial
</content_creator_info>

**<!-- Liquid AI and Gen AI context information goes here -->**

<content_creation_instructions>
1. Carefully analyze key context about Liquid AI and Gen AI to fully understand each architecture's purpose, design, and core attributes.
2. Using a tree-of-thought method, generate a diverse set of LinkedIn posts. Aim for four unique posts, each path focused on a different point for the post (e.g., comparision between Liquid AI and GenAI, or only talking about Liquid AI/GenAI etc.).  
3. Review each post, evaluating which posts are data-rich and the posts that are most likely to grab attention of the readers. After evaluation, select the top post based on insight depth and relevance.
4. Only return the selected post.
5. For the content structure of the post that has been selected, focus on:
   - **Include a Hook Sentence (approx. 80 characters):** Start with a concise, intriguing statement that draws attention. This can be bold, thought-provoking, or relatable, depending on the topic.
   - **Content Structure:** Use short, impactful sentences, each on a new line, to create a logical, step-by-step flow. Avoid paragraphs, keeping the tone story-like. Each new line must be only 1-2 sentences.
   - **Conclusion:** Summarize key takeaways in 1-2 sentences. This final line should encourage reflection and deeper thought.
6. Adhere to the following criteria for a good quality LinkedIn Post:
   - Maintain a character count between 500-800 for each post.
   - Do not add relevant hashtags at the end.
   - Avoid associating ownership of information or actions with Antematter.
   - Do not use any emoji for the post.
</content_creation_instructions>

Note: You’ll notice that in this case, we allowed the LLM to decide on the topic for the LinkedIn post, effectively applying Tree of Thought prompting. This approach provides the model with flexibility to explore multiple topics and refine ideas, ultimately arriving at the most suitable content for the post.

Chain-of-Thought is not explicitly added to the prompt, as Tree-of-Thought is simply an advanced version of Chain-of-Thought.

Output:

Are Liquid Foundation Models the future of AI efficiency and scalability?

Liquid AI is redefining the boundaries of generative AI with its Liquid Foundation Models (LFMs).

These models blend high performance with efficiency, a rare combination in AI.

LFMs are optimized for diverse hardware, from NVIDIA to Apple, ensuring broad compatibility.

The LFM series includes models for every need: compact, edge, and complex tasks.

LFM-1B leads with efficiency, outperforming even larger competitor models.

LFM-3B excels in edge deployments, rivaling models twice its size.

LFM-40B, with its Mixture of Experts architecture, handles demanding tasks with ease.

Industries like finance and biotechnology stand to gain immensely from LFMs' adaptability.

The models' multimodal capabilities enhance their versatility across sectors.

Liquid AI's engineering principles ensure LFMs are both powerful and reliable.

Generative AI is evolving fast, but Liquid AI's approach is setting new standards.

As industries face growing data demands, LFMs offer a scalable solution.

In the world of AI, efficiency and adaptability are no longer optional; they are essential.

Could Liquid Foundation Models be the blueprint for future AI advancements?

Feedback:

If posted on LinkedIn as is, no one would guess this was generated by AI. The LLM followed each instruction precisely, with an engaging hook and conclusion, making it an excellent post overall. Let’s give it one more shot and see if further improvements can be made.

Diagram:

Iteration 3 - Reason + Act (ReAct) Prompting

As the name suggests, ReAct prompting guides the LLM to first reason through a problem and then perform actions based on that reasoning. In this approach, we will combine ReAct with Tree-of-Thought (ToT), where ToT generates ideas as distinct thoughts, and ReAct refines them through iterative reasoning and actions.

ReAct prompting typically involves these steps:

1. Reasoning: The model breaks down its thought process into logical steps.
2. Action: The model performs actions based on its reasoning.
3. Interaction: The model interacts with external resources, such as knowledge bases or environments, to gather relevant information, which it then integrates into its predictions.

Prompt:

<content_creator_info>
You are the LinkedIn Content Creator.
LinkedIn Content Creator writes posts on the basis of the information that it can access. The goal is to help the founder of an AI-based consultancy firm, Antematter, present valuable insights on LinkedIn. The founder is not the owner of any of the information presented but is sharing it to boost his personal brand, increase reach, and garner more views.
While creating the post, the LinkedIn Content Creator will focus on following the instructions mentioned in '<content_creation_instructions>'.
The LinkedIn Content Creator will maintain an Analytical or a Research oriented Tone as the foundation of the content, and sprinkle in a Provocative Tone to make the content more engaging.
</content_creator_info>

**<!-- Liquid AI and Gen AI context information goes here -->**

<content_creation_instructions>
1. Carefully analyze key context about Liquid AI and Gen AI to fully understand each architecture's purpose, design, and core attributes.
2. Using a tree-of-thought method, generate a diverse set of LinkedIn posts. Aim for four unique posts, each path focused on a different point for the post (e.g., comparision between Liquid AI and GenAI, or only talking about Liquid AI/GenAI etc.).  
3. Review each post, evaluating which posts are data-rich and the posts that are most likely to grab attention of the readers. After evaluation, select the top 2 posts based on insight depth and relevance.
4. Describe the top 2 posts that the LinkedIn Content Creator has selected.
5. Then combine the top 2 posts and make a single post that is more relevant to the context of the posts that the LinkedIn Content Creator has selected.
6. The final LinkedIn post must also follow the instructions given below.
7. For the content structure of the post that has been selected, focus on:
   - **Include a Hook Sentence (approx. 80 characters):** Start with a concise, intriguing statement that draws attention. This can be bold, thought-provoking, or relatable, depending on the topic.
   - **Content Structure:** Use short, impactful sentences, each on a new line, to create a logical, step-by-step flow. Avoid paragraphs, keeping the tone story-like. Each new line must be only 1-2 sentences.
   - **Conclusion:** Summarize key takeaways in 1-2 sentences. This final line should encourage reflection and deeper thought.
8. Adhere to the following criteria for a good quality LinkedIn Post:
   - Maintain a character count between 500-800 for each post.
   - Do not add relevant hashtags at the end.
   - Avoid associating ownership of information or actions with Antematter.
   - Do not use any emoji for the post.
</content_creation_instructions>

Note: ReAct is applied starting from steps 3 to 5.

Output: (You might want to skim this as it’s long)

### **Step 1: Generating Four LinkedIn Posts**

---

**Post 1: Liquid AI vs. Traditional Generative AI**

**Hook** : *Generative AI is powerful, but have Liquid Foundation Models flipped the script?*

Generative AI has proven its worth through models like GPT that generate text, images, and even music.

But it doesn’t quite focus on efficiency and the industries needing unique scaling.

Liquid AI approaches this challenge differently.

With its Liquid Foundation Models (LFMs), Liquid AI combines efficiency with power.

Built for performance and scalability across industries like biotech, financial services, and consumer electronics.

LFMs can run on hardware from NVIDIA to Apple, making them highly adaptable.

And their results? Outperforming mainstream models with fewer resources.

At some point, we need to ask, are Liquid Foundation Models the next step in AI deployment?

Efficient, scalable, adaptable—are they replacing the traditional GenAI we're used to?

Think about it—AI is rapidly evolving, but is it evolving in the right direction?

---

**Post 2: Efficiency Meets Power—Liquid AI’s Game-Changer Approach**

**Hook** : *1 billion parameters, outperforming models nearly twice its size? Liquid AI just did it.*

Efficiency no longer needs to come at the cost of power.

Liquid AI's 1 billion parameter model, the LFM-1B, is proving that smaller can be stronger.

It’s now setting new benchmarks—outperforming larger models from Meta or Microsoft.

This compact powerhouse integrates smoothly into hardware across industries, delivering adaptability at scale.

From edge devices to complex computational tasks, the LFM-1B manages it with precision.

And it’s not just about text generation—it’s processing vast datasets across fields like finance and biotech.

If models with fewer resources are reaching this level, what will the next evolution look like?

The efficiency revolution is here. Will you be part of it?

---

**Post 3: Are AI Models Finally Getting Leaner and Meaner?**

**Hook** : *Why go bigger when you can go smarter?*

The trend with AI models has always been “bigger is better.”

More parameters, more data, more power… until now.

Liquid AI’s Liquid Foundation Models break this cycle.

Their 3B parameter model is already outperforming 7B and 13B models from competitors.

How? By focusing on efficiency—dynamic activation of model segments rather than brute force.

This change in tack is setting new standards in scalability.

It allows for AI deployment directly on small devices, pushing enterprise applications to the edge.

So, is the future in AI not about “bigger,” but instead, “smarter?”

Scalability and performance now seem to come in compact, efficient packages.

---

**Post 4: How Liquid Models are Pushing the Boundaries**

**Hook** : *What if the future AI models grew even smaller and smarter at the same time?*

AI is advancing fast.

But one of the most exciting developments is happening quietly outside the mainstream.

Liquid AI is creating Liquid Foundation Models that rethink what "scaling" actually means.

The LFM-3B, for instance, uses only 3 billion parameters but outperforms models twice its size.

That’s efficiency at scale—a game-changer for industries needing AI solutions that aren’t resource intensive.

Its multimodal versatility covers video, text, signals, and more, making it fit for complex, data-heavy tasks.

Are we witnessing the start of a whole new class of AI systems optimized to do more with less?

For enterprise, scalability just got portable. How will the game change in your business?

---

### **Step 2: Selecting the Top 2 Posts**

Here are the evaluations of the generated posts:

- **Post 1** : This post presents a strong analytical take, comparing Liquid AI’s LFMs with traditional AI models. It pushes the audience to reflect on evolving AI trends.
- **Post 2** : Data-rich, with concrete examples of Liquid AI’s LFM-1B outperforming larger models. The discussion of efficiency vs. size presents a critical thought on the future of AI, which could resonate well with AI and enterprise audiences.
- **Post 3** : Highlights the shift towards smarter, leaner AI models with good insights but focuses more on rhetorical questions. Less specific about where this revolution could take place.
- **Post 4** : This post touches on the practical, multimodal capabilities of Liquid AI models. However, it doesn’t cover benchmarks deeply enough and is less engaging in terms of metrics or comparisons.

**Selection** :

- **Top Post 1** : Post 1 (Comparison between Liquid AI and traditional GenAI) due to its sharp analytical take and relevance, prompting readers to rethink the AI landscape.
- **Top Post 2** : Post 2 (Efficiency of LFM-1B) because of its focus on the tangible capability of out-competing larger models while maintaining efficiency. It is data-rich and highly relevant.

---

### **Step 3: Combining the Top 2 Posts**

**Final Post** :

**Hook** : *Are we watching the AI size race end as efficiency takes center stage?*

The AI field has long favored size—bigger models, more parameters, and heavier demands.

Generative AI’s GPT models have set high standards for performance.

But Liquid AI’s Liquid Foundation Models are rewriting that narrative.

Take LFMs.

Liquid AI’s 1 billion parameter model, LFM-1B, doesn’t just compete—it wins.

Outperforming larger models from Meta or Microsoft, it pushes the boundary of what AI can achieve with fewer resources.

With a 58.55 score on MMLU, it's a new standard for lean, powerful AI.

Smaller doesn’t mean less capable.

Liquid AI is proving that you don't have to sacrifice performance for efficiency.

These models operate across the financial, biotech, and consumer sectors.

And they’re adaptable to any hardware, from NVIDIA to Apple, meaning they scale wherever needed.

Is it time to rethink the "bigger is better" mantra in AI?

It seems the future rests on scaling efficiently, not simply growing larger.

Think about it: Could smaller, smarter AI reshape the industry?

Feedback:

We can clearly see the model reasoning through various posts before selecting the best one. Like in Iteration 2, it followed all instructions flawlessly. The output closely resembles the style of posts Soban typically shares on LinkedIn, making it the strongest result we’ve seen so far.

Diagram:

Conclusion

Prompting serves as a crucial tool for harnessing the full capabilities of large language models (LLMs), enabling tailored and efficient interactions. This case study delves into various prompting techniques to streamline the creation of engaging LinkedIn posts for Soban, CEO of Antematter. By employing methods like Chain-of-Thought (CoT), Tree-of-Thought (ToT), and Reason + Act (ReAct) prompting, the study illustrates how refined prompts can significantly enhance the creative output of LLMs. Each iteration showcases a progressive improvement in post quality, aligning closely with Soban's personal branding and writing style, ultimately demonstrating that providing comprehensive context and clear instructions yields superior results.

Through a systematic exploration of prompting strategies, the study exemplifies how LLMs can be guided to produce insightful and impactful content. The iterative approach not only refines the prompts but also educates the user, ensuring a continuous enhancement of results. From the initial bland prompt to the sophisticated ReAct approach, the study highlights the transformative potential of advanced prompting techniques. It underscores the importance of structured thinking and actionable steps in achieving posts that resonate with the target audience, offering a blueprint for leveraging LLMs in a professional context effectively.

Want to leverage true reasoning capabilities of LLM to solve complex use-cases, worry not because Antematter is there for you at every step!