Knowledge-source preparation
Define the scope, dependencies and acceptance criteria for knowledge-source preparation in the context of rag development.
AI Engineering
RAG Development
Connect language models to approved knowledge with retrieval and evaluation.
At a glance
Start with the operational need
Connect language models to approved knowledge with retrieval and evaluation.
For rag 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 rag development depends on the users, integrations and operational constraints involved.
Chunking and retrieval design
Prepare approved knowledge, preserve source permissions and test whether answers can be traced to the right material.
Permission-aware answers
Set clear access rules and review points around the work and information in the system.
Grounding and evaluation
Test representative inputs, record errors and set the boundaries for human approval or escalation.
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 rag 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 rag 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.
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