Summary Building Your Own Product Copilot Challenges and Opportunities arxiv.org
9,719 words - PDF document - View PDF document
One Line
Software engineers face challenges such as higher operational costs and the necessity of guidelines when incorporating AI into products.
Slides
Slide Presentation (8 slides)
Key Points
- Software engineers are facing challenges integrating AI-powered technology into products due to the lack of tools and processes that can handle the scale involved.
- Large-language models (LLMs) are gaining attention in NLP tasks for their ability to comprehend and generate text, but using larger models for inference comes with higher operational costs.
- Interaction with language models involves prompt engineering, which is a time-consuming process requiring trial and error and balancing context with token usage.
- Challenges in building product copilots include intent detection, routing workflows, testing adequacy, and leveraging resources for learning about ML.
- Concerns about safety, privacy, compliance, and developer experience arise in integrating AI technology into software products.
- Participants face challenges in navigating the prompt creation process, managing consistency of outputs, and evolving knowledge and best practices.
- Developers seek a unified platform for streamlining the development of intelligent applications and templates for popular applications like Q&A.
Summaries
18 word summary
Integrating AI into products poses challenges for software engineers, including increased operational costs and the need for guidelines.
68 word summary
The integration of advanced AI into products presents challenges for software engineers, as shown in a study with 26 professionals. Large-language models (LLMs) in natural language processing (NLP) tasks increase operational costs. Guidelines and lessons learned are needed for developers to use these models effectively. Orchestration of product copilots involves intent detection, routing workflows, and testing adequacy. Participants seek a unified "one-stop shop" and templates for popular applications.
128 word summary
The integration of advanced AI into products poses challenges for software engineers, revealed in an interview study with 26 professionals. Large-language models (LLMs) in natural language processing (NLP) tasks increase operational costs due to higher computational requirements. Guidelines and lessons learned are needed for developers to use these models effectively. Prompt engineering for language models is time-consuming, involving trial and error and managing prompt assets. Orchestration of product copilots involves intent detection, routing workflows, and testing adequacy. Learning about machine learning (ML) and addressing safety, privacy, and compliance concerns are also highlighted. Participants expressed concerns about prompt fragility and the need for comprehensive tooling and best practices. Developers seek a unified “one-stop shop” and templates for popular applications, as well as integrating diverse tools into a cohesive workflow.
298 word summary
The integration of advanced AI capabilities into products is a growing trend, but software engineers are encountering challenges in this process. An interview study with 26 professional software engineers revealed pain points at every step of the engineering process and the challenges that strained existing development practices. Large-language models (LLMs) have gained attention in natural language processing (NLP) tasks, but using larger models for inference comes with increased operational costs due to higher computational requirements. There is a need for guidelines and lessons learned for developers to use these models effectively.
Interaction with language models involves prompt engineering, which is a time-consuming process involving trial and error, balancing context with using fewer tokens, and managing and tracking prompt assets. Orchestration of product copilots involves intent detection, routing workflows, limitations in commanding, planning, multi-turn workflows, looping, and managing prompt assets. Testing and benchmarks for product copilots present challenges such as creating benchmarks and reaching testing adequacy.
The challenges faced by participants in learning about machine learning (ML) highlight the evolving nature of the ecosystem and the need for accessible resources to support informal learners. Safety, privacy, and compliance concerns were also highlighted, with a focus on ensuring the safety of users and respecting privacy and security in both input and output retrieved from the models.
Participants expressed concerns about the fragility of prompts and managing consistency of outputs and performance across models. They also noted the need for comprehensive tooling and best practices tailored for building AI copilots. Developers are seeking a unified “one-stop shop” to streamline the development of intelligent applications and advocating for templates designed for popular applications, such as Q&A, which would come bundled with essential configurations like hosting setups, prompts, vector databases, and tests. Integrating diverse tools into a cohesive workflow remains a significant challenge.
704 word summary
A race is underway to embed advanced AI capabilities into products, with virtually every large technology company looking to add these capabilities to their software products. However, software engineers are facing challenges integrating AI-powered technology, as software engineering processes and tools have not caught up with the challenges and scale involved with building AI-powered applications. An interview study with 26 professional software engineers responsible for building product copilots at various companies revealed pain points at every step of the engineering process and the challenges that strained existing development practices.
Large-language models (LLMs) are a class of language models characterized by their large sizes, which have gained significant attention in natural language processing (NLP) tasks. These models have the ability to comprehend and generate text, enabling advancements in machine translation, text summarization, question-answering systems, and sentiment analysis. However, the operational cost of using larger models for inference is higher due to the increased computational requirements during execution. As more of these models become available to developers, there is a need for guidelines and lessons learned for developers to use these models to build usable and reliable experiences.
Language models can predict the next word in a sentence based on the context of the preceding words or generate entirely new text that follows the patterns and characteristics of the training data. Interaction with these models involves using prompts to trigger the model's inference process and define the task or objective of the interaction. Prompt engineering is a time-consuming process that involves trial and error, attempts at wrangling prompt output, balancing more context with using fewer tokens, and managing and tracking prompt assets.
Orchestration of product copilots involves intent detection and routing workflows, limitations in commanding, planning and multi-turn workflows, looping and going off-track, and managing and tracking prompt assets. Testing and benchmarks for product copilots present challenges such as every test being a flaky test, creating benchmarks and reaching testing adequacy, and trailblazing learning strategies. Participants had to start their learning process from scratch and leverage the nascent community of practices forming around social media resources.
The learning challenges faced by participants mirrored the experiences of informal learners of ML, with several factors amplifying and complicating these challenges. Learning in ephemeral and volatile situations compounded challenges in learning, as investments in learning resources were not made due to the evolving ecosystem.
Overall, integrating AI-powered technology into software products presents numerous challenges for software engineers at every step of the engineering process. The need for comprehensive tooling and best practices is evident, as well as the need for guidelines and lessons learned for developers to use language models effectively. The challenges faced by participants in learning about ML highlight the evolving nature of the ecosystem and the need for accessible resources to support informal learners.
Challenges in interaction with large language models (LLMs) included navigating a fragile and time-consuming process of prompt creation, creating advanced workflows, and managing complex state and unpredictable behaviors. Safety, privacy, and compliance concerns were also highlighted, with a focus on ensuring the safety of users and respecting privacy and security in both input and output retrieved from the models.
Participants expressed concerns about the fragility of prompts and managing consistency of outputs and performance across models. They also noted the need for comprehensive tooling and best practices tailored for building AI copilots.
Ecosystem support and broader impacts were also discussed, with participants leveraging nascent communities of practices organized through social media and a plethora of examples to learn how to build copilots. However, they still struggled with selecting and integrating tools to meet all the steps necessary in building a copilot.
Developers are seeking a unified “one-stop shop” to streamline the development of intelligent applications. They also advocated for templates designed for popular applications, such as Q&A, which would come bundled with essential configurations like hosting setups, prompts, vector databases, and tests.
Integrating diverse tools into a cohesive workflow remains a significant challenge. Developers are seeking a unified “one-stop shop” to streamline the development of intelligent applications. Initiating such projects also presents its challenges. Developers are advocating for templates designed for popular applications, such as Q&A. These templates would come bundled with essential configurations like hosting setups, prompts, vector databases, and tests.
1017 word summary
A race is underway to embed advanced AI capabilities into products, with virtually every large technology company looking to add these capabilities to their software products. However, software engineers are facing challenges integrating AI-powered technology, as software engineering processes and tools have not caught up with the challenges and scale involved with building AI-powered applications. An interview study with 26 professional software engineers responsible for building product copilots at various companies revealed pain points at every step of the engineering process and the challenges that strained existing development practices.
Large-language models (LLMs) are a class of language models characterized by their large sizes, which have gained significant attention in natural language processing (NLP) tasks. These models have the ability to comprehend and generate text, enabling advancements in machine translation, text summarization, question-answering systems, and sentiment analysis. However, the operational cost of using larger models for inference is higher due to the increased computational requirements during execution. As more of these models become available to developers, there is a need for guidelines and lessons learned for developers to use these models to build usable and reliable experiences.
Language models can predict the next word in a sentence based on the context of the preceding words or generate entirely new text that follows the patterns and characteristics of the training data. Interaction with these models involves using prompts to trigger the model's inference process and define the task or objective of the interaction. Prompt engineering is a time-consuming process that involves trial and error, attempts at wrangling prompt output, balancing more context with using fewer tokens, and managing and tracking prompt assets.
Orchestration of product copilots involves intent detection and routing workflows, limitations in commanding, planning and multi-turn workflows, looping and going off-track, and managing and tracking prompt assets. Testing and benchmarks for product copilots present challenges such as every test being a flaky test, creating benchmarks and reaching testing adequacy, and trailblazing learning strategies. Participants had to start their learning process from scratch and leverage the nascent community of practices forming around social media resources.
The learning challenges faced by participants mirrored the experiences of informal learners of ML, with several factors amplifying and complicating these challenges. Learning in ephemeral and volatile situations compounded challenges in learning, as investments in learning resources were not made due to the evolving ecosystem.
Overall, integrating AI-powered technology into software products presents numerous challenges for software engineers at every step of the engineering process. The need for comprehensive tooling and best practices is evident, as well as the need for guidelines and lessons learned for developers to use language models effectively. The challenges faced by participants in learning about ML highlight the evolving nature of the ecosystem and the need for accessible resources to support informal learners.
The challenges and opportunities of building product copilots are vast and complex. Participants expressed concerns about the longevity of knowledge and new skills, as well as a lack of authoritative information on best practices. Mindshifts in software engineering were also highlighted, as participants realized they needed to fundamentally change their approach to problems and solutions. Safety, privacy, and compliance were significant concerns, with a focus on ensuring the safety of users, maintaining privacy and telemetry constraints, and addressing responsible AI. Developer experience was another area of concern, with difficulties in connecting tools, initiating projects, and the need for more streamlined toolchains.
Challenges in interaction with large language models (LLMs) included navigating a fragile and time-consuming process of prompt creation, creating advanced workflows, and managing complex state and unpredictable behaviors. Testing and validation also posed challenges due to the lack of standardized metrics and the need for custom testing solutions for LLMs. Safety, privacy, and compliance concerns were also highlighted, with a focus on ensuring the safety of users and respecting privacy and security in both input and output retrieved from the models.
The evolution of knowledge and best practices was a significant challenge, as participants highlighted the lack of centralized resources for guidance. The developer experience was also difficult, with challenges in connecting tools, initiating projects, and the need for more streamlined toolchains.
Participants expressed concerns about the fragility of prompts and managing consistency of outputs and performance across models. They also noted the need for comprehensive tooling and best practices tailored for building AI copilots.
Ecosystem support and broader impacts were also discussed, with participants leveraging nascent communities of practices organized through social media and a plethora of examples to learn how to build copilots. However, they still struggled with selecting and integrating tools to meet all the steps necessary in building a copilot.
Participants expressed interest in creating a system that captures direct feedback from crowdsourced evaluators or end-users. They also identified opportunities for supporting prompt engineering needs, such as authoring, validating prompts, tracing and optimizing prompt completions, and using AI as a sounding board while writing and debugging prompts.
Developers are seeking a unified "one-stop shop" to streamline the development of intelligent applications. They also advocated for templates designed for popular applications, such as Q&A, which would come bundled with essential configurations like hosting setups, prompts, vector databases, and tests.
Integrating diverse tools into a cohesive workflow remains a significant challenge. Developers are seeking a unified "one-stop shop" to streamline the development of intelligent applications. Initiating such projects also presents its challenges. Developers are advocating for templates designed for popular applications, such as Q&A. These templates would come bundled with essential configurations like hosting setups, prompts, vector databases, and tests.
Overall, the challenges and opportunities of building product copilots are vast and complex. Participants expressed concerns about the longevity of knowledge and new skills, as well as a lack of authoritative information on best practices. Mindshifts in software engineering were also highlighted, as participants realized they needed to fundamentally change their approach to problems and solutions. Safety, privacy, and compliance were significant concerns, with a focus on ensuring the safety of users, maintaining privacy and telemetry constraints, and addressing responsible AI. Developer experience was another area of concern, with difficulties in connecting tools, initiating projects, and the need for more streamlined toolchains.