Use-case selection
Define the scope, dependencies and acceptance criteria for use-case selection in the context of generative ai development.
AI Engineering
Generative AI Development
Design and integrate generative AI features into products and operations.
At a glance
Start with the operational need
Design and integrate generative AI features into products and operations.
For generative ai development, the useful starting point is the work people do today: where information enters, where decisions happen and where the current system slows them down.
The solution needs to fit existing processes and data, make exceptions visible, and remain understandable to the team that operates it after launch.
What to plan for
Capability areas that matter
The scope of generative ai development depends on the users, integrations and operational constraints involved.
Prompt and response design
Define the scope, dependencies and acceptance criteria for prompt and response design in the context of generative ai development.
Evaluation and guardrails
Test representative inputs, record errors and set the boundaries for human approval or escalation.
Integration into existing workflows
Identify the systems that exchange information, their contracts and how failures are handled.
Engineering decisions
Questions to resolve before delivery
Good outcomes depend on a few explicit decisions about users, data, integration and measurement.
01
What needs to change for users and operators?
Start with the people and processes affected by generative ai development, then define the few changes that matter most.
02
Which systems, data and teams must connect?
Review current platforms, data ownership, access rules and the failure paths between systems.
03
How will value be tested after release?
Agree on acceptance criteria and operational measures before committing to the next release.
Delivery approach
From problem to working system
A clear path from business context to implementation and ongoing improvement.
01
Understand
Clarify the users, business goal, existing systems and constraints.
02
Shape
Map workflows, data, architecture, priorities and a practical delivery plan.
03
Build
Deliver in visible increments with review, testing and integration.
04
Improve
Measure use, resolve friction and evolve the system responsibly.
Common questions
What teams ask before starting
What needs to change for users and operators?
Start with the people and processes affected by generative ai development, then define the few changes that matter most.
Which systems, data and teams must connect?
Review current platforms, data ownership, access rules and the failure paths between systems.
How will value be tested after release?
Agree on acceptance criteria and operational measures before committing to the next release.
Where this connects
Explore related work and expertise
Follow the path from capability to implementation.
01↗
RAG Development
Connect language models to approved knowledge with retrieval and evaluation.
Explore ↗02↗
AI Agents & Automation
Automate multi-step work with clear controls, monitoring and escalation.
Explore ↗03↗
AI Integration
Connect AI services to existing applications, data and business processes.
Explore ↗The next step
Let's discuss your project ↗Let's make the complex work.
Tell us about your product, operational challenge or engineering need.