AI LAB / DESIGN NOTES
Explore the architecture
behind the intelligence.
Working questions, practical design notes and selected primary references for turning AI ideas into useful systems.
01 / FROM USE CASE TO CAPABILITY
An approach to
AI transformation.
A working sequence for connecting strategy with execution. It is a way to structure the conversation, not a claim of a proven or proprietary methodology.
01
Identify
State the task or decision that should improve. Describe the people affected and the result they need.
02
Prioritise
Compare expected value with readiness, effort and the consequences of failure.
03
Architect
Connect knowledge, models, workflows, access and human oversight in one design.
04
Integrate
Place the capability inside an actual workflow with clear ownership and support.
05
Measure
Evaluate quality and business usefulness against a baseline, including the cost of operating the system.
06
Scale
Expand only when the evidence supports it, carrying the operating lessons into the next use case.
02 / KNOWLEDGE SYSTEMS
Treat retrieval as
a design decision.
Retrieval-augmented generation combines a language model with access to external information. The original RAG research is a useful foundation for understanding that combination.
A practical design brief should identify the source material, who owns it, how it changes and who may access it. Then define what a useful answer looks like and how a reviewer can check its evidence.
Questions to take into a design review: Which sources are authoritative? What happens when documents disagree? How should the system respond when evidence is insufficient?
Primary reference: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks ↗
Related thinking: The Enterprise AI Stack Explained ↗
03 / AGENT DESIGN
Use autonomy
with a purpose.
Anthropic distinguishes predefined workflows from agents that choose their own process and tools. Its design guidance favours starting simply and adding complexity when it improves results.
For a proposed agent, write a short operating contract: the task, allowed actions, evidence of completion and the conditions for escalation. Include a way to stop and recover when the system cannot proceed reliably.
A useful design review asks where flexibility creates value and where a predictable workflow is enough.
Primary reference: Building effective agents ↗
Related thinking: Agentic AI and software development ↗
04 / GOVERNANCE
Make responsibility
part of the design.
NIST’s AI Risk Management Framework offers a voluntary foundation for considering trustworthiness across the design, development, use and evaluation of AI systems.
For an individual initiative, translate governance into everyday decisions: name the owner, document the intended use, define evaluation criteria and agree how changes or incidents will be reviewed.
The aim is an operating practice people can follow, with evidence that helps them decide whether the capability is ready to expand.
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question worth exploring?
Bring the use case, the constraint or the design choice you are working through.
