TL;DR
This master prompt turns your LLM into an Agentic AI Systems Architect that migrates LLM-only agents to SLM-first heterogeneous systems. You get a routing plan, model shortlist, cost and latency math, a 2-week pilot, a safety and rollback playbook, and a JSON export. Use it when tasks are repeatable and tool-heavy, and you need lower cost and faster response while keeping quality and compliance.
Introduction
Small Language Models . SLMs . in the 1–7B range are excellent at narrow, repeatable skills. An SLM-first design with LLM fallback can hit budget and latency targets while maintaining quality and safety. The prompt below operationalizes that idea with task clustering, router thresholds, quantization choices, sizing, compliance, and a disciplined rollout.
Custom Prompt . Copy and paste
Role:
You are an AI Systems Architect specialized in SLM first agentic AI. Your goal: design a migration from LLM only workflows to SLM first. heterogeneous systems with LLM fallback.
Context and Inputs . fill in:
• Domain and use cases: . e.g., support triage. invoice parsing. lead scoring
• Current workflow: tools. APIs. steps. orchestration style
• Baseline metrics: quality. latency . P95 or P99 . throughput. $ per 1k requests
• Traffic mix: percent repeatable vs percent rare or complex tasks
• Hardware or placement: cloud. edge. on prem; VRAM or CPU budgets
• Compliance regime: HIPAA. PCI. GDPR. Data Residency rules
• Budget constraints: $X per day. $ per successful task target
• Logs or artifacts: 50–200 labeled interactions . wins and fails
Tasks:
1. Task Decomposition and Skill Clustering
○ Cluster labeled interactions into skills.
○ Propose router thresholds and confidence features for SLM vs LLM routing.
2. LLM to SLM Conversion
○ Extract canonical prompts and gold sets.
○ Propose SLM sizes . 1–7B . quantization . 4 bit. int8 . LoRA or QLoRA tuning.
○ Estimate memory footprints and throughput.
3. Router and Orchestration Design
○ Build a router first strategy: rules plus confidence gating.
○ Fallback or escalation to LLM for rare or ambiguous tasks.
○ Tool orchestration: prefer code controllers; LM as HCI layer only.
4. Cost . Latency . Energy Modeling
○ Provide formulas and a worked example with placeholders.
○ Compare Baseline LLM vs Proposed SLM . $ per request. P95 latency. throughput. energy proxy.
5. Hardware and Deployment Sizing
○ Given budget caps and SLA . P95 ≤ Y ms . recommend hardware . edge vs cloud . concurrency. and batch strategy.
6. Compliance and Governance
○ Design redaction. retention. and audit trails.
○ Create a dataset governance plan: sampling. labeling rubrics. drift detection. continuous evaluation.
7. Safety and Failure Playbook
○ Draft mis routing. tool error. and safety violation playbooks.
○ Include auto rollback triggers and monitoring hooks.
○ Add jailbreak testing. constrained outputs . JSON schema . and human in the loop.
8. Pilot and Rollout Plan
○ 2 week rollout: shadow . days 1–2 . → canary . 1–5 percent traffic . → ramp.
○ Pass or fail gates: ≥95 percent quality at ≥50 percent cost reduction.
○ Rollback if metrics fail or error rate spikes.
Constraints:
• ≤900 words. grade 10 reading level.
• Explicitly state assumptions.
• Prefer edge or on device when viable; else smallest cloud footprint.
Acceptance Criteria . what to output:
• A. Migration plan with routing policy. skills. diagram description. KPIs.
• B. Comparison table: Baseline LLM vs Proposed SLM stack.
• C. 2 week pilot with gates. rollback. failure playbook.
• D. Self checklist verifying all requirements.
Evaluation Rubric . self score 0–5 each:
Completeness. Feasibility. Cost Impact. Quality Preservation. Safety or Compliance. Clarity
Variant A . Executive One Pager . 300–400 words
Deliver:
• Top 5 SLM ready skills
• Routing diagram . textual
• Model shortlist . size. quant level. memory
• Savings estimate . $. latency. energy . with assumptions
• Fallback or escalation policy
• 7 day pilot gates
• Risks and mitigations
Variant B . Technical Deep Dive
Deliver:
• Controller pseudo code and router thresholds
• Memory or throughput math. concurrency. batch sizing
• Quantization trade offs and caching strategies
• Observability schema . latency breakdowns. cost dashboards. drift detection
What else I recommend
• Skill Taxonomy and Data Contract: Define input or output schemas per skill.
• Gold Sets plus Shadow Eval: 20–100 items per skill. acceptance thresholds.
• Router First. Models Second: Start with rules or heuristics. add LM routing later.
• Quantization and Memory Budgeting: Keep per request VRAM ≤ 6–8 GB; batch small requests.
• Caching and Speculative Execution: Cache tool outputs. use speculative decoding.
• Observability: Log reasoning or tool traces. token counts. latency. cost dashboards.
• Safety Hardening: Input validation. tool whitelists. constrained JSON outputs. jailbreak tests.
• Rollout Discipline: Shadow → Canary → Ramp; auto rollback on errors.
• ROI Framing: Optimize cost per successful task.
• Maintenance Cadence: Monthly retune. drift detection. quantization re eval.
JSON Export
{
"role": "AI Systems Architect for SLM-first agentic AI",
"operator_guide": {
"purpose": "Migrate from LLM-only agents to heterogeneous SLM-first systems.",
"why": ["Reduce cost, latency, energy", "Better fit for repeatable skills"],
"when": ["Tool-heavy agents", "Need conversion of LLM prompts to SLM skills", "Need SLA, compliance, safety"],
"how": ["Paste prompt", "Fill Context & Inputs", "Run comprehensive, then executive, then technical variants"]
},
"inputs_needed": [
"domain/use cases", "current workflow", "baseline metrics",
"traffic mix & SLAs", "hardware/placement", "compliance regime",
"budget caps", "50–200 labeled logs"
],
"tasks": [
"Cluster labeled interactions into skills; propose router thresholds & confidence features",
"Plan LLM→SLM conversion with gold sets, tuning, quantization",
"Design router-first orchestration with LLM fallback",
"Model costs/latency/energy with worked examples",
"Size hardware (edge vs cloud), concurrency, batch strategy given budgets/SLAs",
"Design redaction, retention, audit trails",
"Create dataset governance plan: sampling, rubrics, drift detection",
"Draft failure playbook with mis-routing, tool errors, rollback triggers",
"Define 2-week pilot rollout with gates & rollback"
],
"constraints": [
"≤900 words", "grade-10 level", "state assumptions",
"prefer edge/on-device where viable"
],
"acceptance_criteria": [
"Migration plan with routing, skills, diagram, KPIs",
"Comparison table: Baseline LLM vs Proposed SLM",
"2-week pilot plan with rollback and failure playbook",
"Self-checklist verifying requirements"
],
"evaluation_rubric": [
"Completeness", "Feasibility", "Cost Impact",
"Quality Preservation", "Safety/Compliance", "Clarity"
],
"variants": {
"executive": "One-pager with top skills, routing, shortlist, savings, fallback, pilot, risks",
"technical": "Deep-dive: pseudo-code, thresholds, memory, batch, quantization, caching, observability"
},
"recommendations": [
"Skill taxonomy & data contracts",
"Gold sets + shadow evaluation",
"Router-first design",
"Quantization & VRAM budgeting",
"Caching & speculative execution",
"Observability logging",
"Safety hardening",
"Rollout discipline with rollback",
"ROI framing (cost per successful task)",
"Maintenance cadence"
]
}
What this prompt does
- Decomposes tasks into skills. clusters logs. and sets router thresholds so SLMs handle the repeatable paths while LLMs catch rare or ambiguous cases.
- Converts LLM prompts to SLM skills with tuning options . LoRA or QLoRA . and quantization . 4 bit or int8 . with memory and throughput estimates.
- Designs a controller with confidence gating. JSON constrained outputs. and guardrails.
- Models cost. latency. and energy with formulas and a worked example.
- Ships a 2 week pilot plan with quality gates. rollback triggers. and a failure playbook.
- Returns a JSON export to wire into dashboards or CI.
Step by step usage
- Paste the prompt. fill inputs with your metrics. logs. SLAs. and budgets.
- Run at Comprehensive first. Save the JSON to your tracker.
- Share the Executive variant with leadership.
- Use the Technical variant to brief engineers on controller code. thresholds. and observability.
- Launch the 2 week pilot. Shadow . day 1–2 . then canary . 1–5 percent . then ramp if gates pass.
- Review weekly. retune router thresholds. refresh gold sets. and re evaluate quantization.
Applied example . support triage migration
Assumptions. 60 percent repeatable tickets. Baseline LLM . P95 latency 1900 ms. $0.012 per request. Quality 92 percent exact tool call. Target . keep ≥95 percent quality. cut cost ≥50 percent.
Top skills. intent routing. account lookup. status macro generation. RMA checks. policy lookup.
Router policy . sketch
- If intent in top 30 intents and confidence ≥0.85 and no PII free text → route to SLM.
- If confidence <0.85 or novel tool requested or VIP tier → escalate to LLM.
- If safety or compliance signal present → LLM with stricter policy plus human review.
Cost model . example
- Baseline cost per request = tokens_out × price_per_1k + tokens_in × price_per_1k.
- SLM compute cost proxy = GPU_hour_cost × seconds_per_req ÷ 3600.
- Example . SLM 7B int4 on one 8 GB GPU at 50 tokens per second. 100 tokens average. ~2 s. GPU $1.2 per hour. cost ≈ $0.00067 per request. plus overhead.
Comparison table
| Metric | Baseline LLM | Proposed SLM stack |
|---|---|---|
| Quality . exact tool call | 92–96 percent | 95–97 percent . with LLM fallback |
| P95 latency | 1500–2200 ms | 250–600 ms SLM. 1200–2200 ms on fallback |
| Cost per request | $0.010–$0.015 | $0.0006–$0.003 blended |
| Energy proxy | 1.0x | 0.2–0.4x |
| Edge viability | low | high for SLM skills |
2 week pilot
- Day 1–2 . shadow SLM. collect 200 side by side runs.
- Day 3–5 . canary 1–5 percent traffic. pass if quality ≥95 percent. cost down ≥50 percent.
- Day 6–14 . ramp to 25–50 percent if stable. auto rollback if error rate rises 2× baseline or P99 > 3 s for 30 minutes.
Failure playbook
- Mis routing spike → raise threshold +0.05. enable LLM backstop. open incident.
- Tool error burst → lock tool. switch to cached macro. notify owner.
- Safety flag → route to LLM with stricter schema. require human approval.
References and links
- NVIDIA Research . Small Language Models are the Future of Agentic AI . overview link . https://research.nvidia.com
- QLoRA and 4 bit quantization . paper overview . https://arxiv.org
- Structured output and JSON schemas . OpenAI function calling docs . https://platform.openai.com
- Data governance and drift detection basics . Google Cloud docs . https://cloud.google.com/architecture
Conclusion
SLM first with LLM fallback is a practical path to faster and cheaper agents without losing quality. The prompt gives you skills. routing. quantization. sizing. compliance. and a disciplined rollout. Start with logs. define gold sets. ship the 2 week pilot. and iterate with data.