Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots

The Substack focuses on large and small language models, natural language understanding, chatbots, and conversational user interfaces. It covers AI agent applications, methods for improving AI performance, and practical tools for developers. Themes include AI decision-making, fine-tuning, data design, and enhancing user-AI interaction.

Large Language Models Small Language Models Natural Language Understanding Chatbots Conversational User Interfaces AI Agents AI Fine-Tuning Data Design AI Interaction

The hottest Substack posts of Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots

And their main takeaways

AI Agents: Exploring Agentic Applications

119 implied HN points • 29 Jul 24
🕹 Technology AI Applications Machine Learning Natural Language Data Tools
  1. Agentic applications are AI systems that can perform tasks and make decisions on their own, using advanced models. They can adapt their actions based on user input and the environment.
  2. OpenAgents is a platform designed to help regular users interact with AI agents easily. It includes different types of agents for data analysis, web browsing, and integrating daily tools.
  3. For these AI agents to work well, they need to be user-friendly, quick, and handle mistakes gracefully. This is important to ensure that everyone can use them, not just tech experts.

LangChain Based Plan & Execute AI Agent With GPT-4o-mini

99 implied HN points • 26 Jul 24
🕹 Technology AI NLP Machine Learning Software Development Programming
  1. The Plan-and-Solve method helps break tasks into smaller steps before executing them. This makes it easier to handle complex jobs.
  2. Chain-of-Thought prompting can sometimes fail due to calculation errors and misunderstandings, but newer methods like Plan-and-Solve are designed to fix these issues.
  3. A LangChain program allows you to create an AI agent to help plan and execute tasks efficiently using the GPT-4o-mini model.

Flows Are So Back

39 implied HN points • 22 Aug 24
🕹 Technology AI Data Development Software User Interface
  1. Graphs help show complicated data in a simple way. By using nodes and edges, you can easily see how everything connects.
  2. No-code tools let anyone, even those without programming skills, create complex workflows. This makes development quicker and more accessible for everyone.
  3. There's a growing need for tools that can organize and connect different AI flows. This would help everything work better together and solve problems more effectively.

27 Unique Dev Challenges: A Recent Study Explored the Top Challenges Faced by LLM Developers

39 implied HN points • 20 Aug 24
🕹 Technology AI Development API Integration Data Management Security Issues
  1. Developers face many challenges when working with large language models (LLMs), including issues with API calls and integrating them into existing systems.
  2. Common problems also involve managing large datasets and ensuring data privacy and security while using LLMs for tasks like text generation.
  3. Understanding unpredictable outputs from LLMs is essential, as it affects the reliability and performance of applications built with these models.

LangGraph Agents By LangChain

39 implied HN points • 19 Aug 24
🕹 Technology AI Data Software Programming Engineering
  1. Graph-based representations are becoming popular in AI, making it easier to visualize application flows and manage data relationships. This helps in understanding complex connections between data points.
  2. There are two ways to create graph representations: one is using code to create a visual flow, and the other is using a graphical user interface (GUI) to build the flow directly. This dual approach caters to different needs and levels of user expertise.
  3. Graph data structures allow for both firm control over applications and the flexibility needed for agent-based systems. This is useful for tasks where interactions and decisions must adapt based on inputs or user approvals.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

WeKnow-RAG

39 implied HN points • 16 Aug 24
🕹 Technology AI Data science Machine Learning Natural Language Processing Information Retrieval
  1. WeKnow-RAG uses a smart approach to gather information that mixes simple facts from its knowledge base with data found on the web. This helps improve the accuracy of answers given to users.
  2. This system includes a self-check feature, which allows it to assess how confident it is in the information it provides. This helps to reduce mistakes and improve quality.
  3. Knowledge Graphs are important because they organize information in a clear way, allowing the system to find the right data quickly and effectively, no matter what type of question is asked.

Creating Synthetic Training Data

59 implied HN points • 01 Aug 24
🕹 Technology Artificial Intelligence Data science Machine Learning Natural Language Processing Software Development
  1. Creating synthetic data is hard because it's not just about making more data; it also needs to be diverse and varied. It's tough to make sure there are enough different examples.
  2. Using a seed corpus can limit how varied the synthetic data is. If the starting data isn't diverse, the generated data won't be either.
  3. A new approach called Persona Hub uses a billion different personas to create varied synthetic data. This helps in generating high-quality, interesting content across various situations.

OpenAI Acquired Rockset

59 implied HN points • 31 Jul 24
🕹 Technology AI Data Analytics Infrastructure Applications
  1. OpenAI bought Rockset to make their data retrieval system better, which helps in using AI more effectively.
  2. The acquisition shows that LLMs are being seen more like a tool, and the focus is shifting to building useful applications using these technologies.
  3. Rockset's technology will help OpenAI work better with developers and make it easier to access and use real-time data for AI products.

OpenAI Enhanced Their API With Robust Structured Output Capabilities

39 implied HN points • 12 Aug 24
🕹 Technology AI API Data Development NLP
  1. OpenAI has improved its API to ensure that outputs always match a set JSON format. This helps developers know exactly what kind of data they will get back.
  2. The previous method of generating JSON outputs was inconsistent, making it hard to use in real-world applications. Now, there's a more reliable way to create structured outputs.
  3. Developers can now use features like Function Calling and a new response format to make their apps interact better with AI, ensuring clearer communication between systems.

LangChain Search AI Agent Using GPT-4o-mini

59 implied HN points • 25 Jul 24
🕹 Technology AI Software Web Development Machine Learning Open Source
  1. The LangChain Search AI Agent uses a tool called Tavily API to search the web and answer questions. It breaks down complex questions into simpler sub-questions for better results.
  2. The GPT-4o-mini model is designed to be fast and cost-effective, making it suitable for tasks that require quick responses. It supports both text and vision inputs, expanding its usability.
  3. Using LangSmith, you can track the execution and costs of each step in processing queries. This feature helps in optimizing the performance of the AI agent.

Five Levels Of AI Agents

119 implied HN points • 16 May 24
🕹 Technology AI Software Machine Learning Automation Data science
  1. AI agents can make decisions and take actions based on their environment. They operate at different levels of complexity, with level one being simple rule-based systems.
  2. Currently, AI agents are improving rapidly, sitting at levels two and three, where they can automate tasks and manage sequences of actions effectively.
  3. The future of AI agents is bright, as they will be more integrated into various industries, but we need to consider issues like accountability and ethics when designing and implementing them.

AI Agents With Human In The Loop

19 implied HN points • 15 Aug 24
🕹 Technology AI Automation Chatbots NLP Frameworks
  1. AI agents can now include human input at important points, which helps make their actions safer and more reliable. This way, humans can step in when needed without taking over the whole process.
  2. LangGraph is a new tool that helps organize and manage how these AI agents work. It uses a graph approach to show steps and allows for better oversight and control.
  3. By combining automation with human checks, we can create more efficient systems that still have the safety of human involvement. This lets us enjoy the benefits of AI while also addressing concerns about its autonomy.

RAG Implementations Fail Due To Insufficient Focus On Question Intent

39 implied HN points • 18 Jul 24
🕹 Technology AI NLP Data science Machine Learning Knowledge Graphs
  1. Large Language Models (LLMs) can create useful text but often struggle with specific knowledge-based questions. They need better ways to understand the question's intent.
  2. Retrieval-augmented generation (RAG) systems try to solve this by using extra knowledge from sources like knowledge graphs, but they still make many mistakes.
  3. The Mindful-RAG approach focuses on understanding the question's intent more clearly and finding the right context in knowledge graphs to improve answers.

RAG Foundry By Intel

19 implied HN points • 13 Aug 24
🕹 Technology Artificial Intelligence Software Development Open Source Data science Machine Learning
  1. RAG Foundry is an open-source framework that helps make the use of Retrieval-Augmented Generation systems easier. It brings together data creation, model training, and evaluation into one workflow.
  2. This framework allows for the fine-tuning of large language models like Llama-3 and Phi-3, improving their performance with better, task-specific data.
  3. There is a growing trend in using synthetic data for training models, which helps create tailored datasets that match specific needs or tasks better.

LangSmith, LangGraph Cloud & LangGraph Studio

39 implied HN points • 15 Jul 24
🕹 Technology AI Development Software Tools Data Management Generative AI Cloud Computing
  1. There's a shift in generative AI, moving away from just powerful models to more practical user applications. This includes a focus on using data better with tools that help manage these models.
  2. New tools like LangSmith and LangGraph are designed to help developers visualize and manage their AI applications easily. They allow users to see how their AI works and make changes without needing to code everything from scratch.
  3. We are now seeing a trend towards no-code solutions that make it easier for anyone to create and manage AI applications. This approach is making technology more accessible to people, regardless of their coding skills.

Teaching Small Language Models to Reason

39 implied HN points • 10 Jul 24
🕹 Technology Artificial Intelligence Machine Learning Data science Natural Language Computing
  1. Using Chain-Of-Thought prompting helps large language models think through problems step by step, which makes them more accurate in their answers.
  2. Smaller language models struggle with Chain-Of-Thought prompting and often get confused because they don't have enough knowledge and understanding like the bigger models.
  3. Google Research has a method to teach smaller models by learning from larger ones. This involves using the bigger models to create helpful examples that the smaller models can then learn from.

LangChain Chatbot Framework With Retrievers

99 implied HN points • 07 May 24
🕹 Technology AI Chatbots Development Software Programming
  1. LangChain helps build chatbots that can have smart conversations by using retrievers for specific information. This makes chatbots more useful in different fields.
  2. Retrievers are tools that find documents based on user questions, providing relevant information without needing to store everything. They help the chatbot give accurate answers.
  3. A step-by-step example shows how to use LangChain with Python, making it easier to create a chatbot that answers user inquiries based on real-time data.

Our Human Creativity Is Becoming More Uniform Due To ChatGPT

39 implied HN points • 09 Jul 24
🕹 Technology AI Creativity Innovation Machine Learning Human-Machine Interaction
  1. Using ChatGPT for creativity can lead to less unique ideas among different users. This means many people might come up with similar concepts.
  2. People might feel more creative while using ChatGPT, but this doesn't always result in original or diverse thoughts.
  3. Reliance on a single AI tool can limit the creative process. It's important for new tools to encourage individual input instead of providing complete solutions right away.

Language Agent Tree Search — LATS

59 implied HN points • 12 Jun 24
🕹 Technology AI NLP Automation Software Data
  1. The LATS framework helps create smarter agents that can reason and make decisions in different situations. It's designed to enhance how language models think and plan.
  2. Using external tools and feedback in the LATS framework makes agents better at solving complex problems. This means they can learn from past experiences and improve their responses over time.
  3. LATS allows agents to explore many possible actions and consider different options before making a choice. This flexibility leads to more thoughtful and helpful interactions.

Agent AI: Agentic Applications Are Software Systems With A Foundation Model AI Backbone & Defined Autonomy via Tools

19 implied HN points • 05 Aug 24
🕹 Technology AI Software Models Applications Data
  1. Agentic Applications are advanced software systems that use AI models to operate more independently. They can navigate and process information effectively using tools.
  2. The MindSearch framework helps break down complex questions into simpler parts, making it easier to find answers online. It simulates how humans think and search for information.
  3. There are special agents in this system, like WebPlanner and WebSearcher, that work together to gather and organize information from the web, enhancing the problem-solving process.

LangGraph Studio From LangChain

39 implied HN points • 03 Jul 24
🕹 Technology AI Software Development Cloud Programming
  1. LangGraph helps in creating a flow for conversational applications, allowing for both structured and flexible designs. This means you can manage how chatbots interact without forcing them into a rigid structure.
  2. With LangGraph Studio, users can visualize and control how their AI agents work. It provides tools to track performance, test different scenarios, and optimize interactions effectively.
  3. LangGraph Cloud allows developers to deploy their projects from GitHub and test them in a user-friendly environment. This makes it easier to understand and improve the behavior of AI agents in real-time.

RAG Survey & Available Research

39 implied HN points • 27 Jun 24
🕹 Technology AI Machine Learning Natural Language Data science Deep Learning
  1. Retrieval-Augmented Generation (RAG) mixes retrieval methods with learning systems to help large language models use real-time data.
  2. RAG can enhance the accuracy of language models by incorporating current information, avoiding wrong answers that might come from outdated knowledge.
  3. The framework of RAG includes steps like pre-retrieval, retrieval, post-retrieval, and generation, each contributing to better outputs in language processing tasks.

TinyStories

39 implied HN points • 26 Jun 24
🕹 Technology AI Machine Learning Natural Language Data science User Experience
  1. Phi-3 is a small language model that uses a special dataset called TinyStories. This dataset was designed to help the model create more varied and engaging stories.
  2. TinyStories uses simple vocabulary suitable for young children, focusing on quality over quantity. The stories generated are meant to be both understandable and entertaining.
  3. Training the Phi-3 model with TinyStories can be done quickly and allows for easier fine-tuning. This helps smaller organizations use advanced language models without needing huge resources.

RAG Implementations Are Becoming More Agent-Like

99 implied HN points • 08 Apr 24
🕹 Technology AI Development Software Engineering Data science Machine Learning Automation
  1. RAG implementations are changing to become more like agents, which means they can make better decisions and adapt to different situations.
  2. The structure of prompts is really important now; it’s not just about adding data, but about crafting the prompts to improve how they perform.
  3. Agentic RAG allows for complex tasks by using multiple tools together, making it capable of handling detailed questions that standard RAG cannot.

Phi-3 Is A Small Language Model Which Can Run On Your Phone

39 implied HN points • 19 Jun 24
🕹 Technology AI NLP Machine Learning Data science Software Development
  1. Phi-3 is a small language model that can run directly on your phone, making it accessible for local use instead of needing cloud connections. This means you can use it anywhere without relying on internet speed.
  2. Small language models like Phi-3 are good for specific tasks and regulated industries where data privacy is important. They can provide quick and accurate responses while keeping your data secure.
  3. Training for Phi-3 involves using high-quality data to improve its understanding of language and reasoning skills, allowing it to perform well on par with larger models, despite its smaller size.

The Large Language Model Landscape — Version 5

79 implied HN points • 25 Apr 24
🕹 Technology AI Machine Learning Software Development Natural Language Processing Data science
  1. Large Language Models (LLMs) are evolving with more functionality, combining various tasks into fewer models. This helps in making them more efficient for users.
  2. There are different zones in the LLM landscape, each focusing on specific uses, tools, and applications, ranging from available models to user interfaces.
  3. Tech advancements like prompt engineering and data-centric tools are making it easier to harness the power of LLMs, opening up new opportunities for businesses.

LangGraph From LangChain Explained In Simple Terms

39 implied HN points • 17 Jun 24
🕹 Technology AI Machine Learning Software Development Data Structures
  1. LangGraph helps create clearer conversations by using graphs to map out how dialog flows between different points, making it easier to manage conversations in AI systems.
  2. Prompt chaining connects smaller tasks in a sequence, allowing AI models to handle complex jobs step by step, but can feel rigid like traditional chatbots.
  3. Autonomous Agents bring a higher level of flexibility in how actions are taken, but they can also lead to concerns about having enough control over their decision-making process.

WebVoyager AI Agent

19 implied HN points • 23 Jul 24
🕹 Technology AI Software Web Development Autonomous Systems Machine Learning
  1. AI agents can make their own choices and decide how to reach a goal. They don’t just follow a set plan; they create their own steps as needed.
  2. These agents can try different actions and learn from the results until they find the right answer. They go through a thinking process to solve problems.
  3. While AI agents have some tools to use, they also have limits. If they can't find an answer after trying a few times, they might ask a human for help.

Building The Most Basic LangChain Chatbot

59 implied HN points • 06 May 24
🕹 Technology AI Software Chatbots NLP Data
  1. Chatbots use Natural Language Understanding (NLU) to figure out what users want by detecting their intentions and important information.
  2. With Large Language Models (LLMs), chatbots can understand and respond to conversations more naturally, moving away from rigid, rule-based systems.
  3. Building a chatbot now involves using advanced techniques like retrieval-augmented generation (RAG) to pull in useful information and provide better answers.

GPT-4o mini

19 implied HN points • 18 Jul 24
🕹 Technology AI NLP Machine Learning Software Development Data science
  1. GPT-4o mini is a new language model that's cheaper and faster than older models. It handles text and images and is great for tasks requiring quick responses.
  2. Small Language Models (SLMs) like GPT-4o mini can run efficiently on devices without relying on the cloud. This helps with costs, privacy, and gives users more control over the technology.
  3. SLMs are designed to be flexible and customizable. They can learn from various types of inputs and can adapt more easily to specific needs.

Agentic AI: Creating An AI Agent Which Can Navigate The Internet

19 implied HN points • 17 Jul 24
🕹 Technology AI Web Development Machine Learning Multimodal models Automation
  1. WebVoyager is an AI agent that can browse the web by analyzing screenshots and deciding what to do next. It works like a human browsing the internet, using both visual and text information.
  2. The agent interacts with webpages by performing actions like clicking, scrolling, and typing. This allows it to complete tasks on websites without needing help from humans.
  3. WebVoyager's ability to handle complex web navigation shows the potential of AI agents to perform useful tasks autonomously. It learns to navigate better by using real-world websites rather than just simplified models.

The Importance Of Granular Data Design For Fine-Tuning

59 implied HN points • 02 May 24
🕹 Technology AI Data science Machine Learning Natural Language Processing Software Development
  1. Granular data design helps improve the behavior and abilities of language models. This means making training data more specific so the models can reason better.
  2. New methods like Partial Answer Masking allow models to learn self-correction. This helps them improve their responses without needing perfect answers in the training data.
  3. Training models with a focus on long context helps them retrieve information more effectively. This approach tackles issues where models can lose important information in lengthy input.

Speculative RAG By Google Research

19 implied HN points • 12 Jul 24
🕹 Technology AI Machine Learning Natural Language Processing Data science Computing
  1. Retrieval Augmented Generation (RAG) is a way to improve answers by using a mix of information from language models and external sources. By doing this, it gives more accurate and timely responses.
  2. The new Speculative RAG method uses a smaller model to quickly create drafts from different pieces of information, letting a larger model check those drafts. This makes the whole process faster and more effective.
  3. Using smaller, specialized language models for drafting helps save on costs and reduces wait times. It can also improve the accuracy of answers without needing extensive training.

Moving From Natural Language Understanding To Mobile UI Understanding

19 implied HN points • 11 Jul 24
🕹 Technology AI Machine Learning User Interface Natural Language
  1. Natural Language Understanding (NLU) helps machines grasp and respond to human language, making sense of unstructured conversations.
  2. The shift to Mobile UI Understanding means we are now focused on understanding what's on mobile screens instead of just conversations.
  3. The Ferret-UI model enables devices to interact with users in a more meaningful way, allowing for richer and more context-aware conversations.

RAG, Hallucination & Structure: Research By ServiceNow

59 implied HN points • 18 Apr 24
🕹 Technology AI Machine Learning Data science Natural Language Software Development
  1. ServiceNow is using a method called Retrieval-Augmented Generation (RAG) to help transform user requests in natural language into structured workflows. This aims to improve how easily users can create workflows without needing deep technical knowledge.
  2. By using RAG, they want to reduce 'hallucination', which is when AI generates wrong or irrelevant info, and make the AI more reliable. This is important for gaining user trust in AI systems.
  3. The study also suggests future improvements, like changing output formats for efficiency and streamlining processes so that users can see steps one at a time, making it easier to follow along.

HILL: Solving for LLM Hallucination & Slop

39 implied HN points • 23 May 24
🕹 Technology AI User Interface NLP Machine Learning Software Development
  1. HILL helps users see when large language models (LLMs) give wrong or misleading answers. It shows which parts of the response might be incorrect.
  2. The system includes different scores that rate the accuracy, credibility, and potential bias of the information. This helps users decide how much to trust the responses.
  3. Feedback from users helped shape HILL's features, making it easier for people to question LLM replies without feeling confused.

Evaluating The Quality Of RAG & Long-Context LLM Output

19 implied HN points • 08 Jul 24
🕹 Technology AI NLP Machine Learning Data science Automation
  1. Evaluating the performance of RAG and long-context LLMs is tough because there isn't a common task to compare them on. This makes it hard to know which system works better.
  2. Salesforce created a new way to test these models called SummHay, where they summarize information from large text collections. The results show that even the best models struggle to match human performance.
  3. RAG systems generally do better at citing sources, while long-context LLMs might capture insights more thoroughly but have citation issues. Choosing between them involves trade-offs.

LLM Disruption in Chatbot Development Frameworks

19 implied HN points • 05 Jul 24
🕹 Technology AI Chatbots Natural Language Programming Development
  1. Large Language Models (LLMs) make chatbots act more like humans, making it easier for developers to create smart bots.
  2. Using LLMs reduces the need for complex programming rules, allowing for quicker chatbot setup for different uses.
  3. Despite the benefits, there are still challenges, like keeping chatbots stable and predictable as they become more advanced.

Improve Conversational UIs Using Social Intelligence

59 implied HN points • 09 Apr 24
🕹 Technology Artificial Intelligence Natural Language User Interface Machine Learning Data science
  1. Social intelligence is important for conversational AIs to feel more human-like. It helps them understand emotions and social cues better.
  2. A good conversational UI needs to consider cognitive, situational, and behavioral intelligence. This means the AI should know what you mean, the context of your words, and how to interact appropriately.
  3. Using more data and different types of information beyond just words can help improve how AIs communicate. This could include things like images and gestures to understand conversations better.

OpenAI Agent Query Planning Using LlamaIndex

99 implied HN points • 05 Feb 24
🕹 Technology AI Software Machine Learning Data science Programming
  1. An OpenAI agent can analyze information from multiple documents at once. This helps create detailed answers to queries based on several sources.
  2. Using the LlamaIndex framework, you can easily set up a system to manage and query PDF documents. This makes finding specific data more efficient.
  3. The agent can summarize financial data, showing how companies like Uber grow revenue over time. This is helpful for understanding trends in business performance.