What This Template Does
Most people build AI workflows that rely on a single model — and when that model has an off day, outputs ship broken, hallucinated, or just plain wrong. This template gives you a router system that chains GPT-4, Claude, and Gemini together with automatic quality checks, so if one model fails or produces garbage, the system catches it and retries with a backup before anything reaches your customer, client, or team. It's built for anyone running AI in production — content pipelines, customer support bots, data extraction tools — who needs reliability without babysitting every output manually.
When to Use It
- You're running an AI workflow in production (customer-facing content, automated reports, support responses) where a single bad output costs you credibility or money
- You've noticed inconsistent quality from one model — like GPT-4 nailing it 90% of the time but occasionally hallucinating facts
- You're building in a no-code tool like Make, Zapier, or n8n and want model redundancy without writing custom code
- You're building a personal, one-off project with no real stakes (a single ChatGPT prompt for brainstorming doesn't need a QA gate — this is overkill for casual use)
How to Use It in 4 Steps
- Set your model hierarchy: Decide which model runs first (usually your most reliable one for the task — GPT-4 for reasoning, Claude for long-context writing, Gemini for multimodal), then rank your backups in order.
- Define your QA gate criteria: Pick 3-5 specific checks the output must pass (factual accuracy, tone match, length, no refusals, format correctness). Be specific — vague criteria like "good quality" won't work.
- Set your failover triggers: Decide exactly what counts as a failure — API timeout, error response, QA score below a threshold (like 7/10), or specific red-flag phrases ("I cannot," "as an AI").
- Build the loop in your platform: In Make/n8n/Zapier, this becomes: Call Model 1 → Run QA Gate Prompt → If pass, ship → If fail, call Model 2 → Repeat → If all fail, alert human.
The Template
MULTI-MODEL API ROUTER CONFIGURATION
TASK: [DESCRIBE THE TASK — e.g., "Generate product descriptions," "Answer customer support tickets," "Summarize legal documents"]
---
MODEL HIERARCHY (in order of priority):
1. Primary Model: [MODEL NAME — e.g., GPT-4]
- Parameters: temperature [VALUE], max tokens [VALUE]
- Use case strength: [WHY THIS MODEL GOES FIRST]
1. Backup Model A: [MODEL NAME — e.g., Claude 3.5 Sonnet]
- Parameters: temperature [VALUE], max tokens [VALUE]
- Triggered when: [CONDITION]
1. Backup Model B: [MODEL NAME — e.g., Gemini 1.5 Pro]
- Parameters: temperature [VALUE], max tokens [VALUE]
- Triggered when: [CONDITION]
---
FAILOVER TRIGGERS (any of these = switch to next model):
- API error or timeout exceeding [X] seconds
- QA Gate score below [THRESHOLD, e.g., 7/10]
- Output contains banned phrases: [LIST — e.g., "I cannot," "as an AI language model"]
- Output length outside acceptable range: [MIN]–[MAX] words/characters
- [ADDITIONAL CUSTOM TRIGGER]
---
QA GATE PROMPT (run this on every output before it ships):
"You are a strict quality control reviewer. Evaluate the following output against these criteria:
1. [CRITERION 1 — e.g., "Factual accuracy: no invented statistics, names, or claims"]
2. [CRITERION 2 — e.g., "Tone matches: [DESCRIBE TONE]"]
3. [CRITERION 3 — e.g., "Format follows the required structure: [DESCRIBE FORMAT]"]
4. [CRITERION 4 — e.g., "No refusals, hedging, or AI disclaimers"]
Output to review:
[INSERT MODEL OUTPUT HERE]
Score each criterion 1-10, then give an overall score. If overall score is below [THRESHOLD], respond with 'FAIL' and explain why in one sentence. If it passes, respond with 'PASS' and the overall score."
---
ESCALATION PATH (if all models fail QA):
- Action: [e.g., "Flag for human review," "Send to Slack channel #ai-escalations," "Retry with modified prompt"]
- Notify: [PERSON/TEAM RESPONSIBLE]
- Log entry format: [TIMESTAMP] | [TASK] | [MODELS ATTEMPTED] | [FINAL STATUS] | [REASON]
---
LOGGING (track this for every run):
- Which model succeeded: [MODEL NAME]
- Number of retries before success: [NUMBER]
- QA score of final output: [SCORE]
- Time to complete: [DURATION]
Filled-In Example
MULTI-MODEL API ROUTER CONFIGURATION
TASK: Generate customer support email responses for Brightline SaaS (project management tool), based on incoming ticket content.
---
MODEL HIERARCHY (in order of priority):
1. Primary Model: GPT-4-turbo
- Parameters: temperature 0.4, max tokens 400
- Use case strength: Best balance of tone consistency and instruction-following for support tickets
1. Backup Model A: Claude 3.5 Sonnet
- Parameters: temperature 0.4, max tokens 400
- Triggered when: GPT-4 times out or QA score falls below 7
1. Backup Model B: Gemini 1.5 Pro
- Parameters: temperature 0.3, max tokens 400
- Triggered when: Both GPT-4 and Claude fail QA or return errors
---
FAILOVER TRIGGERS (any of these = switch to next model):
- API error or timeout exceeding 8 seconds
- QA Gate score below 7/10
- Output contains banned phrases: "I cannot help with that," "as an AI," "I'm just a language model"
- Output length outside acceptable range: 50–250 words
- Response doesn't address the customer's stated issue directly
---
QA GATE PROMPT (run this on every output before it ships):
"You are a strict quality control reviewer for Brightline's customer support team. Evaluate the following output against these criteria:
1. Factual accuracy: does it correctly reference Brightline's actual features (task boards, time tracking, team chat) without inventing functionality that doesn't exist?
2. Tone matches: warm, professional, concise — never robotic or overly apologetic
3. Format follows the required structure: greeting, direct answer, next step or resolution, sign-off
4. No refusals, hedging, or AI disclaimers
Output to review:
[INSERT MODEL OUTPUT HERE]
Score each criterion 1-10, then give an overall score. If overall score is below 7, respond with 'FAIL' and explain why in one sentence. If it passes, respond with 'PASS' and the overall score."
---
ESCALATION PATH (if all models fail QA):
- Action: Flag ticket for human review — do not auto-send
- Notify: #support-escalations Slack channel, tag @support-lead
- Log entry format: [TIMESTAMP] | [TICKET #4521] | [MODELS ATTEMPTED: GPT-4, Claude, Gemini] | [STATUS: Escalated] | [REASON: All three outputs scored below 6 on factual accuracy]
---
LOGGING (track this for every run):
- Which model succeeded: Claude 3.5 Sonnet
- Number of retries before success: 1 (GPT-4 timed out)
- QA score of final output: 8.5/10
- Time to complete: 11.2 seconds
Customization Tips
- For solo developers using no-code tools: Change the "FAILOVER TRIGGERS" section to use simple conditional logic blocks in Make or n8n (IF/THEN nodes) instead of custom code — the QA Gate Prompt still runs as a separate API call that feeds into that conditional.
- For teams handling sensitive content (legal, medical, financial): Add a mandatory human review step between the QA Gate and the escalation path, even when outputs pass — set your QA threshold higher (9/10 instead of 7/10) and log every single pass/fail, not just failures.
- To make it more formal: Swap "strict quality control reviewer" in the QA Gate Prompt for "senior compliance auditor" and add a fifth criterion specifically checking for regulatory language requirements in your industry.