Robert Chandler, the CTO at Wordware speaking at GitHub
TLDRRobert Chandler, CTO at Wordware, speaks at GitHub about the potential of AI agents in software development. He introduces Wordware as a new programming language where prompts are the core abstraction, enabling the creation of reusable AI agents. Chandler demonstrates Wordware's capabilities, including a 'Hello World' demo and a complex example using Retrieval-Augmented Generation (RAG) for querying legal contracts. He also showcases an agent that writes and runs code to perform tasks autonomously, illustrating the power and flexibility of Wordware for various applications. The presentation emphasizes the collaborative potential between domain experts and engineers to build innovative AI solutions.
Takeaways
- 😀 Robert Chandler, the CTO at Wordware, discusses the use of prompts to distill expert knowledge into AI agents, particularly in software development.
- 🔧 Wordware is described as a new programming language where the core abstraction is the prompt, allowing for the creation of AI workflows and agents.
- 🛠 The platform aims to be accessible to non-engineers, emphasizing the importance of domain expertise in building effective AI agents.
- 📝 Chandler demonstrates how Wordware can be used to build 'Hello World' prompts and integrate with other tools, like Notion, to create a user-friendly experience.
- 🔎 Wordware enables the creation of agents that can interact with large datasets, such as legal contracts, by using the platform's capabilities to query and analyze information.
- 🤖 The script showcases an advanced use case of Wordware, where it is used to build an agent capable of writing and running code to perform tasks like fetching the current Bitcoin price.
- 🔗 Chandler highlights the importance of chaining prompts to get better outputs from language models, which can lead to more accurate and relevant AI responses.
- 📈 The transcript illustrates the potential of using Wordware to build AI agents that can perform complex tasks, such as creating a personal website by combining various models and data sources.
- 💼 The presentation suggests that Wordware can help bridge the gap between domain experts and engineers, allowing for faster iteration and development of AI solutions.
- 📚 Chandler emphasizes the need for AI agents across a broader spectrum of industries, not just in software development, to leverage the full potential of expert knowledge.
- 🌐 The demo concludes with an invitation for those interested in building with Wordware or for those looking for opportunities as engineers to join the team.
Q & A
Who is the speaker at the GitHub event?
-The speaker is Robert Chandler, the CTO at Wordware.
What is the main theme of Robert Chandler's talk?
-The main theme of the talk is the use of prompts and agents in software development, and how Wordware is facilitating the creation of AI agents and workflows.
What does Robert Chandler suggest is currently lacking in the industry?
-He suggests that there is a lack of AI agents across the broad spectrum and the longtail of industries, which is something he wants to help change.
How does Wordware differ from traditional programming languages according to the talk?
-Wordware is described as a new programming language where the core abstraction is the prompt, allowing users to build AI agents and workflows.
What is the significance of the 'Hello World' demo in the context of Wordware?
-The 'Hello World' demo illustrates the basic functionality of Wordware, showing how prompts are abstracted into functions and used to generate outputs from a language model.
What role do non-engineers play in the development of AI agents according to the transcript?
-Non-engineers, such as PMs and CEOs, can be better at developing AI agents because they have more knowledge about the subject matter they are building.
How does Wordware integrate with other tools and APIs?
-Wordware allows users to make fetch requests to any API and integrate with tools like Zilliz's database, demonstrating its flexibility and usability.
What is the purpose of using a Retrieval-Augmented Generation (RAG) model in Wordware?
-The purpose of using a RAG model is to improve the context and relevance of the answers generated by the system, especially when dealing with complex queries.
Can Wordware be used to build agents that perform multi-step tasks?
-Yes, Wordware can be used to build agents that perform multi-step tasks by chaining together prompts and utilizing various language models.
What is an example of a complex agent built in Wordware as mentioned in the transcript?
-An example is an agent that writes code and runs it to create its own tools, which can then be reused for tasks like finding the current Bitcoin price.
How does Wordware enable rapid iteration and development of AI agents?
-Wordware enables rapid iteration by providing a simple and flexible framework for chaining prompts and integrating with various APIs and language models.
Outlines
🤖 Building AI Agents with Wordware
The speaker introduces the concept of using prompts to create AI agents, emphasizing the current trend of software development agents due to engineers being domain experts in building software. The speaker proposes a broader application of agents across various industries and introduces 'wordware' as a new programming language centered around prompts. Wordware allows users to build AI agents and workflows without needing engineering skills, making it accessible to domain experts. The speaker demonstrates the use of wordware with a 'Hello World' example, showcasing its simplicity and the ability to integrate with other tools and language models.
🔍 Enhancing Language Models with Advanced RAG Techniques
The speaker discusses the limitations of Retrieval-Augmented Generation (RAG) when the query and the answer are not in the same embedding space. To address this, the speaker presents an advanced RAG technique that transforms the query into a form more likely to be found in the source data. This involves generating a chain of thought to identify relevant clauses, transforming the query into snippets that better match the embedding space of the source data, and then extracting and answering the query. The speaker demonstrates this process with a live example, showing how to improve the accuracy of answers from language models by chaining prompts and using RAG effectively.
🚀 Creating Dynamic Agents for Specific Use Cases
The speaker presents a demonstration of building a dynamic agent capable of writing and executing code to perform tasks such as querying APIs and generating content. The agent is designed with a thought-action-input-observation loop, which allows it to search the web, write code, and interact with various APIs. The speaker illustrates this with an example where the agent searches for the current Bitcoin price, selects an appropriate API, and retrieves the price. The demonstration highlights the potential for creating narrow, trans-specific agents that can be rapidly developed and iterated upon in collaboration with domain experts and engineers.
🛠 Rapid Iteration and Application of Wordware
The speaker concludes with an example of rapid application development using wordware, showcasing the speed at which agents can be built and iterated upon. The example involves creating a personal website for a person by leveraging multiple models to fetch and summarize information, generating an image with Stable Diffusion, writing a poem, and hosting the website. The speaker emphasizes the ease and speed of developing such applications in wordware and invites those interested in improving their company's iteration cycles or those looking for opportunities in engineering to connect with them.
Mindmap
Keywords
💡CTO
💡AI agents
💡Prompt
💡Orchestration layer
💡Language model
💡RAG
💡Embedding space
💡Chain of thought
💡API
💡Stable Diffusion
Highlights
Robert Chandler, CTO at Wordware, discusses the potential of using AI agents in software development.
AI agents can distill expert knowledge into reusable components.
There is a current trend of AI agents in software development due to the domain expertise of engineers.
Wordware is introduced as a new programming language with prompts as its core abstraction.
Wordware allows users to build AI agents and workflows without needing to be engineers.
The usability of Wordware is emphasized for PMs, CEOs, and domain experts.
A 'Hello World' example demonstrates the simplicity of using prompts in Wordware.
Wordware's integration with language models and APIs is showcased.
The potential of Wordware to transform queries for better results from language models is discussed.
A demonstration of using Wordware to analyze a contract with a RAG (Retrieval-Augmented Generation) model.
The importance of query transformation for aligning with source data's embedding space is highlighted.
Wordware's capability to chain prompts for advanced RAG operations is shown.
An example of building an agent in Wordware that writes and runs code to create tools.
The agent's ability to use Google search and execute code within a thought-action-input-observation loop.
A demonstration of creating a personal website using multiple AI models and Wordware.
The speed of iterating and building specific agents in Wordware is emphasized.
Wordware's aim to build narrow, trans-specific agents in collaboration with domain experts and engineers.
An invitation for PMs, engineers, and builders to collaborate or join the Wordware team.
