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The early returns on gen AI for software development

CIO Business Intelligence

Early use cases include code generation and documentation, test case generation and test automation, as well as code optimization and refactoring, among others. The maturity of any development organization can easily be measured in terms of the size and type of investment made in QA,” he says.

Software 128
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Expectations vs. reality: A real-world check on generative AI

CIO Business Intelligence

Pilots can offer value beyond just experimentation, of course. McKinsey reports that industrial design teams using LLM-powered summaries of user research and AI-generated images for ideation and experimentation sometimes see a reduction upward of 70% in product development cycle times. What are you measuring?

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Bringing an AI Product to Market

O'Reilly on Data

Without clarity in metrics, it’s impossible to do meaningful experimentation. AI PMs must ensure that experimentation occurs during three phases of the product lifecycle: Phase 1: Concept During the concept phase, it’s important to determine if it’s even possible for an AI product “ intervention ” to move an upstream business metric.

Marketing 361
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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

Since ChatGPT is built from large language models that are trained against massive data sets (mostly business documents, internal text repositories, and similar resources) within your organization, consequently attention must be given to the stability, accessibility, and reliability of those resources. Test early and often.

Strategy 290
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Get AI in the hands of your employees

CIO Business Intelligence

We’ve seen an ongoing iteration of experimentation with a number of promising pilots in production,” he says. Samsara employees are applying these general-purpose assistants to a variety of use cases, like writing documentation and job descriptions, debugging code, or writing API endpoints.

KPI 88
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Try semantic search with the Amazon OpenSearch Service vector engine

AWS Big Data

Lexical search looks for words in the documents that appear in the queries. Background A search engine is a special kind of database, allowing you to store documents and data and then run queries to retrieve the most relevant ones. OpenSearch Service supports a variety of search and relevance ranking techniques.

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Amazon OpenSearch Service search enhancements: 2023 roundup

AWS Big Data

Lexical search In lexical search, the search engine compares the words in the search query to the words in the documents, matching word for word. Semantic search doesn’t match individual query terms—it finds documents whose vector embedding is near the query’s embedding in the vector space and therefore semantically similar to the query.