The Secret to Building AI-Powered Tools | Summary and Q&A

TL;DR

2022 witnessed significant growth in AI, with Sourcegraph discussing the integration of language models in their code understanding engine.

Key Insights

• 👨‍💻 AI has experienced significant growth in 2022, with Sourcegraph's code understanding engine, Kodi, adding value by leveraging language models while addressing challenges like hallucination.
• 👨‍💻 The next evolution in code-building tools with language models should focus on providing relevant and introspectable context, aiding pattern matching and improving coding results.
• 👏 Integrating AI raises questions about the use of different models, security, privacy, and cost, requiring flexible solutions to cater to diverse customer needs.
• 👨‍🔬 Language models complement search engines, making them more valuable as AI enhances data search capabilities.
• 👻 Sourcegraph's approach emphasizes combining language models with code understanding capabilities, allowing different models to be utilized and empowering customers to make informed decisions.
• 🤳 Security and privacy concerns are vital for enterprises, prompting Sourcegraph to offer a self-hostable solution to ensure code understanding without compromising proprietary information.
• 🇨🇷 The cost aspect of using different language models must be considered, requiring transparent communication of pricing and value propositions to customers.

Transcript

2022 was a breakout year for AI in fact many have even claimed that Chachi BT is the fastest growing app of all time so with so much opportunity on the table AI is the topic of conversation in every boardroom as CEOs figure out how to best integrate this new superpower but they're also asking really important questions around data privacy competiti... Read More

Questions & Answers

Q: What is Kodi, Sourcegraph's editor extension, and how does it address the challenges of using large language models?

Kodi is a chat-based interface that allows code searching and provides valuable context. Sourcegraph utilizes its code understanding engine to fact-check and offer relevant context, combating the tendency for language models to hallucinate false information.

Q: What distinguishes Sourcegraph's approach from other code-building tools that utilize language models, like GitHub Copilot?

Sourcegraph aims to go beyond autocomplete-driven context by providing more relevant and introspectable context. They consider actions like finding references and examples of API usage, giving users a better understanding of their code and aiding in pattern matching.

Q: How does Sourcegraph address security and privacy concerns when integrating AI models, especially for enterprise customers?

Sourcegraph acknowledges the sensitivity enterprises have towards security and privacy. They offer a self-hostable solution to allow code understanding without compromising data. By providing options to use different language models and plugging them into specific code sections, they cater to customers with varying risk and security profiles.

Q: How does Sourcegraph approach the cost aspect of using different language models and the impact it has on customers?

Sourcegraph recognizes the evolving nature of AI models, each with varying costs, security postures, and effectiveness. They aim to offer access to multiple models, transparently relaying the pros and cons of each to customers. The value lies in combining language models with context and code understanding capabilities.

Summary

In this video, the speaker discusses the breakout of AI in 2022 and its impact on various industries. They also address important questions around data privacy, competition, cost, and accuracy. Additionally, they explore the role of Sourcegraph's product, Kodi, as an editor extension that leverages language models to provide relevant context and accurate information. The speaker further delves into the usage of different language models like GitHub Copilot and Ghostwriter, while emphasizing the importance of incorporating more relevant context and introspection into AI tools. They also touch upon the integration of various models, the implications for security and privacy, and the flexible approach Sourcegraph adopts in allowing customers to choose different models based on their needs and requirements.

Questions & Answers

Q: What is the main focus of Sourcegraph's editor extension called Kodi?

The extension is designed as a chat-based interface that allows users to search for code and context within the code. It takes advantage of language models' power while addressing the challenge of hallucination by providing accurate and contextually relevant information. Sourcegraph's extensive context capabilities make it an ideal fact-checker and context provider to the language model.

Q: Are there other tools available that enable code building using language models?

Yes, there are tools like GitHub Copilot and Ghostwriter that assist in code generation using language models. However, these tools primarily rely on autocompletion driven by recent file history in the editor. Sourcegraph believes that the next evolution of these tools involves providing more relevant context, similar to how a human programmer seeks context by exploring code examples, finding references, and utilizing API information.

Q: How does Cody differ from existing code generation tools?

Cody aims to deliver better results by offering more relevant and introspectable context. While existing tools leverage recent file history for autocompletion, Cody goes beyond this local context and incorporates features like go-to definition, finding references, and providing examples of API usage. Cody also offers transparency by indicating the files it refers to and allowing users to flag any incorrect or misleading information, making it a more robust and reliable tool.

Q: How does Sourcegraph consider the use of different language models by competing companies and address security and privacy concerns?

Sourcegraph recognizes the varied risk and security profiles of their diverse customer base, ranging from conservative enterprises to fast-moving startups. They adopt a flexible approach by allowing customers to bring their own language models to the platform. Currently, customers can choose from models like Claude and Chachi BT, with plans to integrate additional models in the future. By providing selection options and transparently presenting the pros and cons of each model, Sourcegraph ensures that customers can make informed decisions based on their specific needs and requirements.

Q: How does the cost of different language models affect Sourcegraph and its customers?

Each language model carries a different cost, and Sourcegraph acknowledges the evolving nature of AI models and the potential price variations. By offering a range of models, Sourcegraph caters to a broader customer base and prevents them from being tied to a single provider. Customers can weigh the cost, security posture, efficacy, and specialty of each model to determine the most suitable option for their use case. This approach enables Sourcegraph to adapt to market changes and provide value by combining language models with their expertise in structured code understanding.

Takeaways

The breakout of AI in 2022 has sparked numerous discussions and considerations for companies across various industries. Integration of AI, specifically language models, presents opportunities and challenges related to data privacy, competition, cost, and accuracy. Sourcegraph's editor extension, Kodi, aims to provide accurate and relevant information by leveraging language models while addressing the challenge of hallucination. They differentiate themselves by offering more extensive context and introspectable results, surpassing the limitations of existing code generation tools. Integrating different language models allows customers to choose based on their risk, security, and cost preferences. By combining language models with their code understanding capabilities, Sourcegraph provides a flexible and valuable solution for AI integration in different organizations.

Summary & Key Takeaways

• Sourcegraph introduces Kodi, an editor extension that combines chat-based interface and code searching capabilities to leverage language models while addressing challenges such as hallucination.

• Tools like GitHub Copilot and Replit's Ghostwriter use autocomplete-driven context fetch, but Sourcegraph aims to provide more relevant and context-rich information for better coding results.

• Integrating AI and language models raises questions about using various models, security, privacy, and cost, to cater to diverse customer needs.