January 2024 Summaries

Suggested Summaries is a new feature that utilizes AI to provide incident responders with accurate and up-to-date summaries, ensuring they have the necessary information when joining an ongoing situation. The project lead for this feature, Milly, shares insights into its development process, including challenges faced, learning prompt engineering, and more. This feature aims to improve communication and efficiency during incidents by keeping responders informed with accurate summaries.

Financial services companies often require effective incident management systems due to the critical nature of their operations. In the case of Bud Financial, building an internal incident response tool initially seemed like a good idea for Michael Cullum and his team. However, they soon encountered limitations in this approach. As a result, they decided to adopt incident.io as a replacement for their Slackbot, which ultimately led to significant savings in terms of resources, money, and time. This transformation greatly improved the way Bud Financial managed incidents. Chris Evans, CPO of incident.io, discusses Michael's decision-making process in a case study.

The text discusses the process of running projects for AI heavy features. It highlights an experimental approach, starting with a proof of concept, early dogfooding, prompt refinement, and establishing a feedback loop to improve the feature. The author emphasizes the importance of understanding the capabilities of LLMs like OpenAI's GPTs and adjusting approaches accordingly. They also mention the need to know when to stop refining prompts and focus on customer feedback for continuous improvement.

Recently, the company launched a major AI product consisting of four smaller projects: related incidents, suggested summaries, suggested follow-ups, and an assistant feature. Product Engineers Rob and Isaac played crucial roles in building out the "related incidents" project. They discuss their experiences, including the challenges faced during prompt engineering and project timelines. Further details on each feature will be provided in upcoming mini-episodes.

The text discusses how vector embeddings can be used to find relationships in data and highlights a project that utilized embeddings to power incident-related features. It explains the concept of embeddings as an array of numbers representing a model's interpretation of a given block of text, and how they can be used for search, clustering, recommendations, and anomaly detection. The author shares their experience in using OpenAI's API for generating embeddings and discusses prompt engineering, measuring prompt effectiveness, and running the feature in production. They also cover storing embeddings using Postgres extension pgvector and handling prompt and model changes. Finally, they share how the concept of linking incidents together proved valuable and inspired users to manually do it themselves.

This week, incident.io launched four new AI-powered features to enhance incident management. These include Assistant, Suggested Summaries, Suggested Follow-ups, and Related Incidents. Ed Dean, Product Analyst, and Charlie Revett, Product Engineer, discuss the development process and impact of these features in an episode of The Debrief. They emphasize balancing autonomy with AI, working with design partners, and learning prompt engineering while building a product rooted in this practice.

A team of data scientists and engineers developed a GPT-style AI Assistant for historical incident analysis. They started by creating a proof-of-concept (PoC) in one week to test the feasibility of using an assistant to help users create complex visualizations and derive insights from them. OpenAI's release of their API, Assistants, provided new possibilities for the project as it allowed the creation of segregated assistants with intimate knowledge of incident data. The team then focused on improving performance and error rate metrics during a two-week period. After gathering feedback from beta users, they made final adjustments before launching the feature to all customers.

The text discusses a company's journey to building their first AI feature, which involved using OpenAI's models to suggest incident summaries for an incident management tool called incident.io. They share lessons learned from this process, including the importance of human input, prompt engineering challenges, and the need for clear communication with customers about data usage. The company also emphasizes the potential benefits of incorporating AI features into products across various industries.

Incident management can be challenging due to the complexity of incidents and the need for quick decision-making. To address these issues, a series of AI-powered features have been launched to help incident responders save time, learn from previous incidents, and become more resilient over time. These features include Assistant, which allows users to explore and learn from their incident history using natural language; Suggested Summaries, which automatically propose new summaries for incident updates; Related Incidents, which find past incidents similar to the current one by understanding context, people involved, and steps taken to resolve; and Suggested Follow-ups, which recommend action items after an incident is closed. These AI-powered features have received positive feedback from customers, who appreciate their ability to improve incident management experience.

The text discusses the process of debugging and optimizing the performance of a Go compiler in a large codebase. It highlights the challenges faced with monolithic architectures, such as increased compile times, which can slow down feature shipping and affect developer productivity. To address these issues, the author uses various tools and flags provided by Golang to visualize the build process and identify bottlenecks. By analyzing the trace output from the Go compiler, the author identifies areas for improvement, such as reducing dependencies and breaking up large packages into smaller ones. The text concludes with a lesson on using developer tools like compilers to improve performance and productivity.