Your prompts are your key to success 🔐
How to create them effectively?
Why does the idea of creating personalized prompts in the ChatGPT environment often elicit hesitancy? Is it the sheer volume of pre-existing templates that overwhelm us, or is it a general lack of understanding?
Let's consider the benefits of crafting your own prompts. These are not just customizable strings of text; they're potent tools capable of solving your specific problems. Personalized prompts can take on the work you find monotonous, liberating your time and boosting your productivity.
Creating your own prompts is akin to unlocking a new level of understanding and interaction with AI. When you design a prompt, you're not just issuing commands; you're developing a nuanced understanding of how your input can produce desired outputs. It's a learning experience that, while steep, is immensely rewarding.
So, where do you begin this journey? Start by asking yourself key questions: What is my end goal? Who is the intended user? How detailed should the prompt be? What specific outcome am I expecting? And crucially, is it secure in terms of data protection?
To illustrate the process, let's say you have a specific problem you want to solve. You begin by defining your desired output, and from there, you reverse-engineer the steps needed to reach it. A well-crafted prompt can help ChatGPT do everything from organizing data to condensing lengthy articles into concise bullet points.
I'll provide a deeper dive into this fascinating subject in future discussions and even supply some practical exercises for you to try. Remember, taking control of your prompts is not just an exercise in customization—it's about reaching a level of understanding that dissipates any initial fear or hesitation.
🗝️ Quick Bytes:
Twelve Labs Unveils Pegasus-1 for Video-to-Text Transformation
Twelve Labs introduces Pegasus-1, a cutting-edge video-language foundation model with 80B parameters and three integrated components: video encoder, video-language alignment model, and language decoder. The product line-up also includes new Video-to-Text APIs such as Gist API, Summary API, and Generate API.
The company's unique "Video First" philosophy incorporates four core principles: Efficient Long-form Video Processing, Multimodal Understanding, Video-native Embeddings, and Deep Alignment between Video and Language Embeddings. Twelve Labs has amassed over 300 million meticulously curated video-text pairs for training, making it one of the largest video-text corpora available.
Regarding performance, Pegasus-1 significantly outshines existing models, showing a 61% relative improvement on the MSR-VTT Dataset and 47% enhancement on the Video Descriptions Dataset as per QEFVC Quality Score. Additionally, when compared to leading ASR+LLM models like Whisper-ChatGPT, it outperforms by 79% on MSR-VTT and 188% on the Video Descriptions dataset.
Apple's Catch-Up Game in AI Landscape
After initially missing the AI wave dominated by products like ChatGPT, Apple is aggressively working on generative AI technology, spearheaded by senior VPs John Giannandrea and Craig Federighi. The company is set to invest approximately $1 billion per year to develop more innovative versions of Siri and AI functionalities in its next iOS.
Apple's efforts extend beyond voice assistants; the company integrates generative AI into development tools like Xcode, aiming for functionality similar to Microsoft's GitHub Copilot. Eddy Cue's team is also exploring AI-driven features for Apple Music and productivity apps, mirroring innovations by competitors like Spotify and Microsoft.
A key internal debate is the deployment strategy of generative AI—whether it should be purely on-device for faster processing and better privacy, or cloud-based for more advanced operations. Given the pace of industry changes, a hybrid approach may be likely, emphasizing the high stakes for Apple in adapting to the generative AI landscape.
OpenAI's Skyrocketing Valuation Amid AI Boom
OpenAI is negotiating a deal that could triple its valuation to $80 billion, making it one of the world's most valuable tech startups. Led by Thrive Capital, the tender offer for existing shares would place OpenAI behind only ByteDance and SpaceX in terms of valuation, according to CB Insights data.
The AI sector continues to attract significant investment. Amazon invested up to $4 billion in Anthropic, OpenAI's competitor, while other AI startups like Cohere and Inflection AI have raised hundreds of millions. Microsoft has invested a total of $13 billion in OpenAI.
The surge in valuations underscores investor confidence in the potential of generative AI technologies, initially popularized by OpenAI's ChatGPT. These technologies are set to disrupt various industries, from search engines to digital education, but the capability to build them remains concentrated among a few well-funded companies.
🎛️ ChatGPT Command Line
This is the mistake that 98% ChatGPT users make.
Don't be one of them.
I will show you why using prompt templates might not be the best approach for you, unlike what many LinkedIn gurus might suggest. Prompt templates offer a quick and convenient way to interact with Chat GPT. They are pre-made sets of instructions that only require minimal input from you to produce a desired output. Sounds great, right?
Well, slower.
Yes, prompt templates are convenient, but they come with their own set of limitations.
For starters, the "one size fits all" nature of templates means they are inherently generic and may not cater to your specific needs or scenarios. This leads to a lack of customization; you can reverse-engineer these templates to a degree, but it's different than having full control over the prompt design.
Consequently, the outputs you receive are often generic and need more nuance you might be looking for. Moreover, with a thorough understanding of the intricacies of these templates, you can avoid misusing them, which could result in less-than-optimal results. Most importantly, relying on these templates can stifle your learning curve, robbing you of the chance to fully understand the capabilities and limitations of Chat GPT.
On the flip side, crafting your own custom prompts offers a lot of advantages that go beyond mere convenience. For instance, the high level of customization allows you to tailor the prompts to your specific needs, ensuring that you solve your unique problems more effectively.
This personalized approach yields better results and gives you a sense of ownership and control. You'll understand how your prompts function, granting you complete control over the interaction and the output you receive from Chat GPT.
If you're starting out with Chat GPT, using readily available prompt templates is tempting. However, take the time to understand how prompts work and create your own. This will give you a more fulfilling experience and enable you to leverage Chat GPT more effectively.
If you need help, send me a message - I would go with you through this process and also, I would love to share my presentation with you from this webinar.
💡Explained
Enhancing LLMs Reasoning Abilities with Step-Back Prompting ✨
After the first year with LLMs, we already have a plenty of methods that show how to improve LLM outputs when forming an instruction. The most common include Chain-of-Thought, Tree-of-Thought, adding a few examples of required output (few-shot prompting) and others. Recently, a new paper from Google Deepmind showed an interesting approach called Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models.
🤖 How step-back prompting works?
The idea of step-back prompting is to mimic the process of how people solve tough problems. It divides a difficult task into two parts: first, thinking big picture (Abstraction), and then solving the problem (Reasoning):
Abstraction: Instead of directly addressing the question, the model is prompted to ask a high-level, abstract question related to the task. This step distills complex tasks into broader concepts or principles. For example, if the original question is about the school Estella Leopold attended during a specific period, a step-back question might inquire about her "education history."
Reasoning: Once the high-level concept or principle is established, the model leverages its intrinsic reasoning abilities to derive the solution to the original question, grounded in the facts related to the abstract concept. This is termed "Abstraction-grounded Reasoning."
💡Advantages of step-back prompting
Improved Performance: Empirical experiments have shown that step-back prompting improves performance across various complex reasoning tasks, including knowledge-intensive QA, multi-hop reasoning, and science questions. By breaking down tasks into abstraction and reasoning, the model reduces the risk of reasoning failures during intermediate steps.
Sample-Efficient Teaching: Abstraction is an easier skill to teach to Large Language Models (LLMs) through sample-efficient demonstrations. This implies that enhancing reasoning abilities through abstraction is a feasible and effective approach.
Reduction of Complexity: step-back prompting reduces the overall complexity of a task, making it more manageable for LLMs. This is particularly valuable for questions that involve a myriad of details or intricate constraints.
💭 Keep in mind
Step-back Prompting is Not Always Necessary: Abstraction is not required in all scenarios. For straightforward questions with readily available answers or questions related to fundamental principles, introducing abstraction may not significantly impact the model's performance.
Challenging Reasoning: Despite the benefits, reasoning remains a challenging skill for LLMs to acquire, even after applying step-back prompting. Reasoning failures still persist, especially in complex tasks.
🎓 Learning more
If you are interested in learning more about Prompting Techniques I recommend checking the Prompting Guide by DAIR.AI. This hands-on course covers prompt engineering techniques/tools, use cases, exercises, and projects for effectively working and building with LLMs.
Summary
In conclusion, step-back prompting presents a promising approach to enhance the reasoning capabilities of Large Language Models. By breaking down complex tasks into abstraction and reasoning, it addresses some of the limitations inherent in these models. However, it's essential to recognize that abstraction isn't a one-size-fits-all solution and that reasoning challenges persist.
🗞️ Longreads
- The Best Inventions of 2023 by TIME (read)
🎫 Events
Hey there, fellow no-code and low-code enthusiasts! It's Aleksander here, and I'm thrilled to inform you about the upcoming No Code Days 2023 conference this autumn. If you're passionate about creating applications without diving deep into coding, this event is for you! 🤖 Here's what's in store:
💡 Two days jam-packed with insightful lectures.
💡 18 hours of workshops that will inspire and motivate.
💡 3 exciting thematic tracks: No Code & AI / Low Code / Change Path.
💡 Hands-on with the latest No Code / Low Code tools.
📅 Mark your calendars for November 20-21, 2023. 📍 We'll be gathering at Expo XXI, Warsaw.
And here's a little treat for you, my lovely readers: use the promo code KEYGEN to grab your tickets at a 15% discount!
I can't wait to see you there! Let's catch up, exchange high fives, and dive deep into some tech talks. Let me know if you're coming, and we can plan to meet up.