The hottest AI Substack posts right now

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The LangChain Implementation Of DeepMind’s Step-Back Prompting

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 26 Oct 23
🕹 Technology AI
  1. LangChain now has a way to use DeepMind's Step-Back Prompting, which helps improve how AI answers questions. It allows the AI to first rephrase a question into a simpler one before answering.
  2. This process involves creating examples to guide the AI on how to respond. The AI uses these examples to learn how to generate better questions and answers.
  3. You need some specific installations and an OpenAI API Key to try this out in a coding environment. Once set up, you can easily run the Step-Back Prompting in your projects.

A New Prompting Approach From DeepMind Called Analogical Prompting

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 25 Oct 23
🕹 Technology AI
  1. DeepMind's Analogical Prompting helps language models recall similar past problems to solve new ones. This way, models can learn from existing knowledge without needing specific examples every time.
  2. This approach allows models to create their own relevant examples, reducing the need for human labeling and making the problem-solving process more efficient.
  3. By generating tailored examples, DeepMind's method improves the accuracy of solutions while also simplifying the training process for the models.

LLMs & Contextual Demonstration

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 25 Oct 23
🕹 Technology AI
  1. Large Language Models (LLMs) learn from examples in a method called few-shot learning. This means they can understand and perform tasks based on just a few demonstrations.
  2. The effectiveness of LLMs in learning depends on how the input is organized, the types of labels used, and the format in which information is presented. These factors really matter for good performance.
  3. Using good prompts can dramatically improve how well smaller models work, even if they initially seem weak. Proper prompt engineering helps in making these models more effective for various tasks.

Large Language Model (LLM) Disruption of Chatbots

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 23 Oct 23
🕹 Technology AI
  1. Large Language Models (LLMs) are changing the way chatbots are built. They can help improve understanding of what users say by grouping similar questions and making designs easier.
  2. Voice technology is becoming more important in customer support, leading to more complex conversations. This includes using voice recognition and speech synthesis to help handle customer queries.
  3. There are ongoing challenges with trust and privacy when using LLMs. Companies need to make sure they protect personal information while also proving they are using the technology responsibly.

Large Language Model (LLM) Stack — Version 5

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 20 Oct 23
🕹 Technology AI
  1. More open-source LLM models are available, letting people experiment and innovate. This is creating new opportunities for developers to explore different applications.
  2. No-code fine-tuning dashboards are making it easier for users to customize LLMs without technical skills. This expands the functionality of LLMs in various fields.
  3. Basic LLMs are replacing older products, and some advanced models are more at risk in this competitive landscape. This shift highlights the need for improved chat interfaces and prompt engineering techniques.
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The Conversational AI Technology Landscape: Version 5.0

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 19 Oct 23
🕹 Technology AI
  1. The rise of voice technology is changing how chatbots work. Now, they need to handle voice calls and deal with more complex conversations.
  2. Large Language Models are improving chatbot efficiency. They help create training data and can also generate conversations more effectively.
  3. The chatbot market is becoming more complicated. Vendors must adapt to include voice interactions and advanced language processing to stay relevant.

LangSmith Hub By The Numbers

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 17 Oct 23
🕹 Technology AI
  1. LangSmith has four main parts: Projects, Data, Testing, and Hub. The first three are all about improving production, while Hub is for testing before launch.
  2. Chatbots are the most popular use case for using large language models, followed closely by summarization and questions and answers on documents.
  3. OpenAI leads the prompt count in the LangSmith Hub, followed by Anthropic and Google. This shows how important different models are when experimenting with prompts.

Large Language Model Landscape

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 16 Oct 23
🕹 Technology AI
  1. Large Language Models (LLMs) are evolving and diversifying, leading to the rise of Foundation Models that can handle various types of data like text and images. This means they can do more complex tasks now.
  2. There's a shift in how LLMs are used, with a focus on improving their functions like text analysis, speech recognition, and dialog generation. New techniques help these models perform better in their designated tasks.
  3. The market is seeing exciting new opportunities, especially in tools that help businesses use LLMs effectively, like data discovery and user-friendly interfaces. These tools can help companies tap into the potential of LLMs better.

A New Prompt Engineering Technique Has Been Introduced Called Step-Back Prompting

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 12 Oct 23
🕹 Technology AI
  1. Step-Back Prompting helps Large Language Models find better answers by simplifying complex questions. It turns a detailed question into a more generic one that's easier to tackle.
  2. This technique can be combined with other methods to improve accuracy and effectiveness. It shows promise in fixing errors from traditional approaches.
  3. Using Step-Back Prompting requires careful thought and might work best with autonomous systems. It's a more advanced method compared to static prompting.

OpenAI Has Expanded Their Fine-Tuning GUI

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 11 Oct 23
🕹 Technology AI
  1. OpenAI now allows fine-tuning with just 10 records, making it easier and faster to personalize models.
  2. The new graphical user interface (GUI) simplifies the fine-tuning process, making it accessible to more users without needing extensive technical skills.
  3. Costs for fine-tuning have decreased significantly, allowing organizations of all sizes to create customized models.

Fine-Tuning LLMs With Retrieval Augmented Generation (RAG)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 11 Oct 23
🕹 Technology AI
  1. Using Retrieval Augmented Generation (RAG) helps improve how language models work by allowing them to learn from additional, relevant data.
  2. RA-DIT is a new method that combines fine-tuning of the language model with updates to the retriever, making both more aligned and effective.
  3. A human approach to training the retriever with curated data ensures ongoing improvement and better responses in real conversations.

Can LLMs Outperform Humans At Prompt Engineering?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 03 Oct 23
🕹 Technology AI
  1. Recent studies suggest that LLMs (large language models) may be better at creating prompts than humans. This means they can potentially get better results from the same tasks.
  2. The process called Automatic Prompt Engineering (APE) uses input and output examples to generate effective prompts without much human effort. It could change how we interact with LLMs in the future.
  3. Humans might not need to test many prompts anymore since LLMs can create tailored ones. This could make using AI easier and more efficient for everyone.

LLM Drift

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 29 Sep 23
🕹 Technology AI
  1. LLM Drift refers to big changes in how language models respond over a short time. This means their answers can differ quite a bit unexpectedly.
  2. Studies show that the accuracy of models like GPT-3.5 and GPT-4 can go up and down significantly in just a few months. Sometimes they get worse at certain tasks.
  3. It's important to keep checking how these models behave over time because their performance can shift for many reasons, not just from minor tweaks.

Automatic Prompt Engineering

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 27 Sep 23
🕹 Technology AI
  1. Automatic Prompt Engineering (APE) creates prompts for text generation based on what you want as input and output. It helps make the process easier and faster.
  2. With APE, a computer can suggest the best prompts by testing different options and scoring them for quality. This reduces the need for a human to write every prompt manually.
  3. Using APE allows for better interaction with large language models by focusing on user intent and context. It makes conversations feel more natural and responsive.

LLM Drift, Prompt Drift, Chaining & Cascading

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 27 Sep 23
🕹 Technology AI
  1. LLM Drift refers to the changes in the responses of language models over time, where their accuracy can significantly decline.
  2. Prompt Drift happens when the same prompt gives different responses because of changes in the model or data, even if the prompt itself hasn't changed.
  3. Cascading occurs when errors from one part of a process affect subsequent parts, making issues worse as they go along.

What Is The Future Of Prompt Engineering?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 26 Sep 23
🕹 Technology AI
  1. Prompt engineering might not be the main way we interact with AI in the future. It seems we'll use more natural and voice-based communication instead.
  2. Understanding context and reducing ambiguity are key challenges in human-like conversations with AI. This helps the AI to provide better answers.
  3. For businesses, fine-tuning models and using tools like context references help improve AI responses. Both methods work together to make AI better.

Emerging Large Language Model (LLM) Application Architecture

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 19 Sep 23
🕹 Technology AI
  1. Large Language Models (LLMs) work with unstructured data like human conversations. They generate natural language, but can sometimes give incorrect answers, known as 'hallucination.'
  2. Fine-tuning LLMs isn't popular anymore due to high costs and the need for constant updates. Instead, focusing on relevant prompts helps get better, accurate responses.
  3. Using multiple LLMs for different prompts makes sense. New tools are emerging to test how well different models work with specific prompts.

Agents, LLMs & Multihop Question Answering

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 21 Apr 23
🕹 Technology AI
  1. Agents can use different tools based on user requests. This gives them the flexibility to respond to questions that don't fit a typical sequence.
  2. Prompt chaining involves linking prompts together to create a more complex response. However, it can struggle with unexpected user queries.
  3. For better responses, it's important for an Agent to have clear instructions on which tool to use. Fine-tuning these instructions can improve how well the Agent answers questions.

Chain-Of-Thought Prompting In LLMs

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 20 Apr 23
🕹 Technology AI
  1. Chain-of-thought prompting helps large language models break down complex tasks into smaller, manageable steps. This makes it easier for them to solve problems.
  2. Using chain-of-thought reasoning in prompts can improve how well language models perform on tasks by allowing them to show their reasoning process.
  3. This method is especially useful for tasks that require common sense or math, making it similar to how humans approach problem-solving.

OpenAI Playground Start & Restart Prompt Injection

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 19 Apr 23
🕹 Technology AI
  1. OpenAI is using ChatML to help the AI tell the difference between human and machine text. This can reduce bad prompt injections by recognizing who is giving instructions.
  2. They have introduced different modes for specific tasks. Each mode has its own setup to guide users on how to interact with the AI effectively.
  3. New options in OpenAI Playground let users add text at the beginning or end of an AI response. This helps create better conversations and reminds users how to make good prompts.

Scaleable Prompt Pipelines For LLMs

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 18 Apr 23
🕹 Technology AI
  1. Creating good prompts for AI needs context. A well-structured prompt includes clear instructions, context, the user's question, and the expected answer format.
  2. To handle many prompts at once, automation is key. Using tools to automatically search and retrieve the right context for prompts will save time and improve responses.
  3. For AI to work well in specific areas, it needs accurate and well-organized data. This data helps improve the AI’s answers, especially in narrow topics.

The Evolution Of Prompt Engineering

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 17 Apr 23
🕹 Technology AI
  1. Prompt engineering is important for getting the best responses from large language models. Users have to carefully design prompts to mimic what they want the model to generate.
  2. Static prompts can be turned into templates with placeholders that can be filled in later. This makes it easier to reuse and share prompts in different situations.
  3. Prompt pipelines allow users to create more complex applications by linking several prompts together. This helps organize how information is processed and improves user interaction with chatbots.

The Foundation Conversational AI Technologies: Version 3

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 13 Apr 23
🕹 Technology AI
  1. There's been a rise in chatbot development frameworks that now include large language models (LLMs). This means chatbots can do more complex tasks than before.
  2. LLMs are not just for generating responses anymore. They can help create entire conversation flows and assist developers more effectively.
  3. Future improvements will focus on better fine-tuning and supervision methods for LLMs, making them even smarter and more useful.

Generative AI Prompt Pipelines

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 12 Apr 23
🕹 Technology AI
  1. Prompt pipelines make it easier to provide answers by using templates and adding specific context from a knowledge source. This helps to create better responses based on user requests.
  2. When a user asks something, the system finds the right template, fills in the necessary information, and sends it off to get a clear answer quickly.
  3. Using these pipelines helps to avoid mistakes by ensuring the information used is updated and accurate, rather than relying on potentially outdated data.

Foundation Large Language Model Stack

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 05 Apr 23
🕹 Technology AI
  1. Creating a complete chart of large language model products is really hard. There are so many different uses and categories for them.
  2. The landscape of LLMs is changing quickly, with new generative products being revealed every day. Some of these products may not be available yet.
  3. It's important to understand the functionality of each product to categorize and segment them correctly. Feedback from others can help improve this understanding.

5 Things NLU Engines Are Really Good At

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 03 Apr 23
🕹 Technology AI
  1. NLU engines make data entry super easy with no coding needed. You can just click and put in your data without worrying about complicated setups.
  2. Intents, or the goals of what users want, are flexible and can adapt to different classes or categories. This helps in understanding user requests better.
  3. Entities, which represent specific items or information, have improved a lot. Better detection of these lets chatbots gather information without having to ask the user again.

Future OpenAI Functionality Will Supersede Many Current LLM Applications

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 31 Mar 23
🕹 Technology AI
  1. OpenAI's features are expanding rapidly, making it likely that many current applications will become obsolete. Just like when smartphones added flashlight functions, many apps may no longer be needed.
  2. Startups need to really focus on giving users a great experience and unique features to stand out. It's important to build a special software layer that adds real value to their products.
  3. With all the changes happening in LLM technology, companies must adapt quickly. They need to stay flexible and innovative to keep up with what OpenAI and others are doing.

What Constitutes A Large Language Model Application?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 30 Mar 23
🕹 Technology AI
  1. Large Language Models (LLMs) are advanced AI tools that can understand and create human language. They help with tasks like writing, summarizing, and recognizing different pieces of information.
  2. There are different parts to building applications with LLMs. This includes using models, tools for development, and creating apps that end users can interact with.
  3. Prompt engineering is important for getting the best results from LLMs. It involves creating and managing prompts to guide the AI in generating useful responses.

Creating Training Data For Text Classification In Google Cloud Vertex AI

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 27 Mar 23
🕹 Technology AI
  1. Creating training data for AI is a crucial first step in making it work well. It involves careful organization and structuring of data to help the AI learn effectively.
  2. A data-centric approach requires ongoing exploration and refinement of the training data. This means continuously checking the data for patterns and making adjustments as needed.
  3. Using human labelers to categorize data can be costly and complex. It's often easier to automate this process with human oversight rather than sending data out for labeling.

Large Language Models, Generative AI & Google Cloud Vertex AI

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 23 Mar 23
🕹 Technology AI
  1. Large Language Models (LLMs) have two sides: Generative and Predictive. Generative AI is popular for its ease of use, while Predictive AI requires specific training data and high accuracy.
  2. Google Cloud has focused on predictive AI before delving into generative AI. They offer tools for developers to create AI applications quickly, like chatbots and digital assistants.
  3. Classification is a key part of Predictive AI. It involves sorting input into predefined classes, which helps the model understand and respond accurately to user input.

Google Has Started To Open Access To Bard

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 22 Mar 23
🕹 Technology AI
  1. Google has opened access to Bard for users in the U.S. and U.K., with plans to expand to more places soon. You need to apply to access it.
  2. Bard is a helpful AI tool that gives users multiple answers to questions, letting them pick the best option. It works alongside Google Search to provide more information.
  3. Google designed Bard to be simple and efficient, aiming to use its search expertise to ensure users get reliable information and suggestions.

Prompt Engineering, OpenAI & Modes

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 17 Mar 23
🕹 Technology AI
  1. Prompt engineering is really important for getting the most out of large language models. Good prompts can help the model give accurate and relevant responses.
  2. To prevent models from making things up or 'hallucinating,' prompts need to be carefully structured and put together. This helps keep the context clear and the information reliable.
  3. OpenAI is working on improving the safety and quality of responses using better prompt structures. This reduces risks like prompt injection attacks and helps ensure more consistent answers.

OpenAI Has Three New Use Modes, Each With Mode Specific Models

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 16 Mar 23
🕹 Technology AI
  1. OpenAI has introduced three new modes for its language models. Each mode is designed for specific tasks like chat, insertion, and editing.
  2. These modes help users get better results by matching their tasks with the right model. Using the correct mode makes the AI work more effectively.
  3. Prompt engineering is now tailored to each mode. This means users will need to adjust their input templates to fit the specific needs of each mode.

Large Language Models, Foundation Models & Multi-Modal Models

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 13 Mar 23
🕹 Technology AI
  1. Large Language Models (LLMs) are being developed into Foundation Models that can handle tasks beyond just language, like images and voice. This shows how technology is evolving to be more versatile.
  2. GPT-4 is now seen as a Multi-Modal Model that combines different types of data, allowing it to work with text, images, and more. This expands the possibilities for AI applications.
  3. As the use of LLMs increases, there will be more focus on creating fine-tuned models. This means turning unstructured data into structured data for better interaction and understanding.

What Does ChatML Mean For Prompt Chaining Applications

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 09 Mar 23
🕹 Technology AI
  1. Chatbots allow users to input data more freely using natural language. This means people don't have to fit their input into specific forms or buttons.
  2. Prompt engineering helps users create effective prompts for large language models. It involves designing prompts that guide the model to produce the desired responses.
  3. With the introduction of ChatML, there will be a standard way to format prompts. This could make it easier for different applications to understand and process user requests.

Adding A Moderation Layer To Your OpenAI Implementations

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 08 Mar 23
🕹 Technology AI
  1. Adding a moderation layer to OpenAI implementations is essential to comply with usage policies. This helps avoid serious issues like account termination.
  2. The moderation endpoint is free to use and monitors for harmful content like hate, violence, and self-harm. Companies should check their API calls for inappropriate content.
  3. OpenAI is continually improving the moderation tools, but users need to frequently update their own policies to align with these changes. Regular checks can help ensure safe usage.

NLU & NLG Should Go Hand-In-Hand

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 07 Mar 23
🕹 Technology AI
  1. Using NLU and NLG together can make chatbots work better. They can detect what users want and give accurate responses.
  2. Traditional NLU systems still have strong abilities in understanding user intent that shouldn’t be ignored. They're a valuable asset in chatbot design.
  3. Regularly checking and updating the prompts used by chatbots can help improve how they respond to users, making interactions more effective.

When Using The ChatGPT API, Users Will Have To Manage The Context

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 06 Mar 23
🕹 Technology AI
  1. When using the ChatGPT API, users must provide context for the conversation because it doesn't remember past interactions. You need to include previous messages to keep the conversation clear.
  2. If the number of messages exceeds a limit, you can keep only the most recent ones to save space. This way, the model still understands the flow of the conversation.
  3. If you want better responses, you should be clear with your instructions and specify what type of answer you need. Changing how you ask questions can help improve the output.

Example Code & Implementation Considerations For GPT 3.5 Turbo, ChatML & Whisper

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 03 Mar 23
🕹 Technology AI
  1. The GPT-3.5 Turbo model can produce different responses even with the same input because it is non-deterministic. This means you might not get the same answer every time you ask a question.
  2. To maintain context in conversations when using the API, you can use a few-shot approach by providing previous prompts and responses. This helps make the chat feel more natural.
  3. OpenAI's Whisper model can transcribe audio files and can even detect the language of the audio. It has good accuracy rates for several languages, with Spanish and Italian scoring the best.

The Introduction Of Chat Markup Language (ChatML) Is Important For A Number Of Reasons

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 02 Mar 23
🕹 Technology AI
  1. Chat Markup Language (ChatML) helps improve security for large language models by protecting against prompt injection attacks. This means it can make conversations safer and more reliable.
  2. ChatML organizes conversations into roles like system, assistant, and user. This helps clarify who is saying what in the conversation, which can reduce misunderstandings.
  3. The development of ChatML is just starting, and future updates will likely allow it to handle more than just text. It may soon include images, sound, and other data types, making it even more versatile.