Solution Brief

Enterprise-Ready Generative AI Code Generation
Drive developer productivity with Generative AI code generation
Developers have been quick to adopt Generative AI; however, they’re doing thisoutside the purview of IT. While AI-assisted code generation is useful, it is often
done without oversight, security, compliance, or consistency, falling into thecategory of “shadow AI,” which increases risk. Standardizing Generative AI for
coding across the organization presents an opportunity to increase developer
productivity, reduce human error, accelerate development efforts, and upskill
your workers safely.

Transform natural language into effective code
Using natural language inputs, AI-assisted code generation improves
accessibility, lowering the barrier to entry for new programmers and reducing
the time spent on low-value activities for senior developers. Additionally,organizations can ensure that coding is based on known standards andbest practices, enabling organizations to better meet business objectives
and security and compliance requirements. This also leads to more efficient
prototyping, rapid iteration, and better collaboration while taking into
consideration intellectual property and copyright risks.

Intelligent code generation use cases

Natural language programing
Transform natural language into
executable code with Generative AI,
making coding more accessible andaccelerating development efforts.

Unit testing
Automate and scale testing bygenerating diverse use cases thatspeed up testing and help detectbugs and address more scenarios.

Code documentation
Leverage Generative AI to analyze
code and automatically generatetechnical documentation,
including comments, API guides,
and knowledge bases.

Code modernization
Automatically translate andupdate programming languages,
modernizing software while
preserving its original functionality.

Code Generation Use Case - Generative AI© 2024 Dell Inc. or its subsidiaries.

How it works
Organizations can drive enhanced
and consistent AI-assisted code
creation by building strong guardrails
and developing best practices for
AI. This ensures standardization,enhances security, and improvesdeveloper productivity. To do this,organizations should:
Identify and collect ideal
code examples andpreferred processes,
implementing them intocode-writing models.
Train developers on prompt
engineering, helping themincorporate AI tooling into
their workflows.
Review the output of themodel and correct/edit theresults to enable the correctfunctionality.
Monitor and iterate,
incorporating developer
feedback into the model.

Why your data is critical

About Dell Generative AI

AI-assisted code generation is greatly enhanced by the quality, relevance, anddepth of available data, code samples, and repositories. Whether coding, writing
documentation, or unit testing, quality data provides deep context for moreaccurate and reliable results.

Dell Technologies offers the world’s
broadest Generative AI solutions
portfolio, from client devices to datacenter to cloud, all in one place.

Continuous improvement with new data, feedback, guardrails, and processes
is critical to keep pace with innovations in programming languages anddevelopment methodologies. This ensures that the solution remains relevant,compliant, and effective, generating code that incorporates modern practices,standards, and technologies.

You can accelerate your AI journeywith Dell Technologies Services
and a broad partner ecosystemthat includes support for many
open source Generative AI models.
Regardless of whether a teammember is working with purposebuilt Dell infrastructure to powerorganization-wide Generative AI
applications, or simply prompting AI
from a Dell laptop, they are working
with the industry’s broadest and mostadvanced portfolio of AI-enabledsolutions.

AI Code generation on-premises
Tailoring AI-assisted coding solutions to specific on-premises environments
can boost organizational efficiency and productivity. By integrating seamlessly
with existing systems, custom tools enhance control, security, and complianceto meet enterprise standards. Additionally, they provide the scalability andflexibility needed to innovate and maintain a competitive edge.

Data sources

Interface DomainsUse cases

Code generation data sources and use cases
Natural Language
codingUnit testingCode
documentation

Codemodernization

Developmentteams

IT OpsDevOpsSecurity teams

Generative AI toolInternal code
repository

Open source code
repositories

Vendor code
repositories

Best practice
documents

Your organization’s
proprietary libraryof existing codeand code branches,
including indevelopment stems

Collections ofcode that are
free to view anddistribute, including
information on
usage and issues

Many companies(such as Dell andMicrosoft) provide
free access to codedeveloped by theirengineering teams

While not containing
actual stems of code,these assets tell yourmodel how to build
code aligned to yourinternal standards

Learn more about Dellsolutions for Generative AI.

Contact a Dell
Technologies Expert.

Our approach is to bring AI to yourdata. This means meeting strict
data sovereignty and compliancerequirements while right-sizing AI
infrastructure to reduce costs, support
sustainability objectives, and keep you
in control of the model and your data.

Next steps
Get started with a fee-waived
Accelerator Workshop for GenerativeAI, which can help you gain
consensus among your businessand technical stakeholders on yoursolution and prioritized use cases.

Learn more
Accelerator Workshop for
Generative AI

Join the conversation.

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