LLM Configuration & Prompt Design
From default LLM usage to configured outputs that are actionable.
Most teams use LLMs without instructions, prior context, or configuration. The result: generic outputs that don't prioritize, justify, or close with a next step. This track teaches how to configure chats with role, method, quality criteria and output structure — without changing tools.
This is what the operational asset looks like.
Illustrative mockup of the MVP type delivered by this track, with pre/post scorecard.
MVP
Configured system prompts + analysis framework
Before
- LLMs used without prior instructions
- Generic outputs with no prioritization
- Each person gets different quality
After
- Chats configured with role, method and criteria
- Actionable outputs with prioritization and justification
- MECE framework reproducible by the team
Pre / post scorecard
Scale 1–5
What the product shows
How to configure system prompts with role, method and quality criteria
MECE framework for structuring LLM-powered analysis
Output validation against benchmarks and blind spot detection
Prompt design that turns generic suggestions into actionable work tickets
Want this running in your operation?
Let's talk about adapting it to your stack, your team and the KPI you need to move.