ADM can produce different kinds of outputs: short answers, structured summaries, step-by-step investigations, and sometimes visuals/diagrams (depending on what your deployment supports). The most common customer friction is not knowing which model/LLM setting to use and when to prioritize accuracy vs speed.
This guide provides:
- A practical decision framework for selecting an LLM (when configurable)
- Guidance on how to request outputs (text vs structured vs diagrams)
- Safe patterns to reduce hallucinations and improve grounding
How to Access These Features
Depending on your ADM deployment, model selection may appear as:
- A model dropdown in Conversation
- A workflow setting for runs
- An admin/default setting configured by your team
If model selection is not visible in your UI:
- Treat this doc as “how to prompt for the right behavior”
- Ask your admin which models are enabled and how they map to use cases
What “good reasoning” Looks Like in ADM
ADM is most reliable when you:
- Provide asset names, time windows, and what “correct” means
- Ask for evidence (citations or references to sources)
- Ask for assumptions to be listed explicitly
- Request next actions and checks to validate results
Use this pattern:
- What ADM concluded
- What evidence it used
- What it didn’t know / assumptions
- What to do next
LLM selection: Practical Guidance (When You Can Choose)
Use a “fast” Model When
- You need quick summaries or reformatting
- You’re brainstorming questions to ask
- You want a first pass and will validate with evidence
Examples
- “Summarize this incident thread into 10 bullets.”
- “Rewrite this into stakeholder language.”
Use a “reasoning / higher-accuracy” Model When
- You need root cause analysis
- You need careful multi-step logic
- You want fewer wrong turns
Examples
- “Explain why reconciliation mismatched and what changed upstream.”
- “Identify the most likely cause from multiple signals.”
Use a “visual/diagram-capable” Model When (If Supported)
- You need architecture diagrams, flowcharts, or structured visuals
- You want a crisp “one-slide” output for sharing
Examples
- “Create a diagram of the incident flow: detection → impact → mitigation → follow-ups.”
If your ADM does not support diagram generation directly, request a text-based diagram format:
- Mermaid (
flowchart TD ...) - ASCII diagrams
- or a “diagram description” you can paste into a diagram tool
Ask for the Right Output Type (Even Without Model Selection)
When You Want Structured Output
Ask explicitly for:
- Headings
- Tables with specific columns
- Checklists
- Decision trees
Example “Provide: (1) root cause hypothesis list with confidence, (2) evidence per hypothesis, (3) next verification steps, (4) recommended owner team.”
When You Want “high precision”
Add guardrails:
- “Use only retrieved context / cite sources”
- “If unknown, say unknown”
- “List assumptions separately”
Example “Answer using only Knowledge Base citations. If the answer isn’t in sources, say what’s missing and what document we should upload.”
Reducing Hallucinations
Use these techniques:
1) Force Evidence
- “Cite the source sections used.”
- “List which documents were referenced.”
2) Force Constraints
- “Only consider last 7 days.”
- “Only consider production assets.”
3) Force Uncertainty Handling
- “If you’re not sure, provide the top 3 hypotheses and how to validate.”
4) Separate “facts” vs “recommendations”
Ask for two sections:
- Facts observed
- Suggested actions
Common Scenarios + Recommended Model Behavior
Scenario: Business User Asks “Is Finance data healthy today?”
Recommended:
- Concise summary
- Clear status: green/yellow/red
- What changed
- What to do next
- Links to assets/incidents
Prompt: “Give me a finance reliability status update for today: top issues, impacted dashboards, and recommended actions. Keep it under 12 bullets.”
Scenario: Engineer Asks “Why did this policy fail?”
Recommended:
- Deeper reasoning
- Table of failing rules
- Correlation with upstream changes
- Verification steps
Prompt:
“Analyze the failure pattern for customer_validation at 2 PM: show failing rules, affected rows, what changed upstream, and how to verify root cause.”
Scenario: Cross-team Incident Review
Recommended:
- Timeline
- Decisions made
- Action items
- Owners
- Prevention steps
Prompt: “Create a post-incident summary: timeline, root cause, impact, mitigations, and 5 preventive actions with owners.”