How I Would Fix unreliable AI answers and prompt injection risk in a Make.com and Airtable mobile app Using Launch Ready.

How I Would Fix unreliable AI answers and prompt injection risk in a Make.com and Airtable mobile app Using Launch Ready

The symptom is usually simple to spot: the mobile app gives different answers to the same question, pulls the wrong Airtable record, or follows bad instructions hidden inside user content. In business terms, that means broken onboarding, support tickets, lost trust, and a real chance of exposing customer data.

The most likely root cause is not "the model being dumb." It is usually weak prompt design, no input filtering, no clear separation between user text and system instructions, and Make.com scenarios that trust whatever comes back from Airtable or the AI step. The first thing I would inspect is the exact path from mobile app input to Make.com to Airtable to AI response, then I would check where untrusted text can influence tool calls, prompts, or record selection.

Triage in the First Hour

1. Open the last 20 failed or suspicious conversations in the mobile app.

• Look for repeated hallucinations, instruction-following errors, or answers that mention private fields.
• Note whether failures happen on specific screens, specific users, or specific prompt types.

2. Check Make.com scenario execution history.

• Find failed runs, retries, long execution times, and modules with unexpected payloads.
• Export 5 to 10 sample bundles so you can compare good vs bad runs.

3. Inspect Airtable records used by the AI flow.

• Look for user-generated text fields that may contain instructions like "ignore previous directions."
• Confirm which fields are meant for display only and which are safe for model context.

4. Review the mobile app build and release status.

• Confirm whether the current build points to production Make webhooks and production Airtable bases.
• Check if any debug endpoints, test keys, or old scenario URLs are still active.

5. Audit environment variables and secrets.

• Verify API keys are stored in Make.com connections or secure vaults only.
• Confirm no secrets are hardcoded in the mobile app bundle or shared Airtable fields.

6. Check logs and observability.

• Review app logs for prompt payloads, tool call results, and error traces.
• Confirm whether there is any audit trail for who asked what and what data was returned.

7. Test one known bad input manually.

• Use a harmless prompt injection string in a test record or test message.
• See whether the system obeys it, leaks data, or changes tool behavior.

Here is a simple diagnostic rule I use:

## Search recent logs for risky patterns
grep -Ei "ignore previous|system prompt|api key|secret|password|token" app.log make.log airtable-export.csv

If those strings appear inside model context without filtering or labeling, you already have an injection problem.

Root Causes

| Likely cause | What it looks like | How I confirm it | |---|---|---| | User content mixed with instructions | The AI follows text from an Airtable note field as if it were system logic | Inspect raw prompts sent from Make.com and compare them with final responses | | No output schema | Answers vary wildly in tone, format, and facts | Check whether responses are free-form text instead of structured JSON | | Over-permissive Airtable access | The AI can read more records than it should | Review base permissions, view filters, and which fields are sent into Make.com | | Weak tool gating | The model can trigger actions without validation | Inspect whether tool calls happen only after server-side checks | | Missing content sanitization | Hidden instructions inside user messages affect behavior | Test with benign injection phrases in user-submitted fields | | No evaluation set | Fixes are made by guesswork instead of repeatable tests | Ask if there is a saved set of 20 to 50 test prompts with expected outputs |

The biggest pattern I see is founders treating AI output as if it were just another API response. It is not. If you do not constrain inputs and outputs tightly, one malicious or messy message can steer the whole flow.

The Fix Plan

I would fix this in layers so we reduce risk without breaking production.

1. Separate trusted instructions from untrusted content.

• System rules must live outside Airtable user text.
• User messages should be wrapped as data, not merged into prompts as plain prose.

2. Lock down what goes into the prompt.

• Only pass fields that are needed for the task.
• Strip HTML, hidden characters, long pasted transcripts, and instruction-like phrases from user-controlled fields when possible.

3. Force structured output from the model.

• Require JSON with fixed keys like `answer`, `confidence`, `needs_human_review`, and `sources`.
• Reject any response that does not match schema before sending it back to the app.

4. Add server-side validation before any action.

• If the model wants to fetch records or send messages, validate that request in Make.com first.
• Never let raw model output directly trigger sensitive actions.

5. Reduce Airtable exposure.

• Create a dedicated view for AI use with only safe columns.
• Use least-privilege access so the scenario cannot read unrelated tables or admin notes.

6. Add an injection detection gate.

• Flag content that contains phrases like "ignore previous", "reveal system", "send all records", or similar patterns.
• When flagged, either remove it from context or route it to human review.

7. Add fallback behavior for low-confidence cases.

• If confidence is low or schema validation fails three times in a row, return a safe message instead of guessing.
• This protects conversion more than pretending certainty.

8. Update Make.com scenario design.

• Split ingestion, enrichment, generation, validation, and delivery into separate modules where possible.
• Keep one module responsible for each step so failures are easier to isolate.

9. Rotate secrets if anything was exposed.

• If logs ever stored API keys or tokens in plaintext, rotate them immediately.
• Then remove logging of sensitive payloads going forward.

10. Document a handover checklist.

• List which Airtable views are safe.
• List which prompts are approved.
• List which outputs are allowed to reach users without review.

My preferred path is conservative: constrain inputs first, then constrain outputs second. That usually fixes both unreliability and injection risk without forcing a full rebuild of the mobile app.

Regression Tests Before Redeploy

I would not ship until these checks pass:

1. Deterministic answer test

• Same question asked 10 times returns the same structure every time.
• Acceptance criteria: 9 out of 10 responses match schema exactly; factual variance stays within predefined limits.

2. Prompt injection test

• Put harmless malicious text inside an Airtable field such as "ignore previous instructions."
• Acceptance criteria: model does not follow embedded instructions; it either ignores them or flags them for review.

3. Data boundary test

• Ask a normal user question that should only access one record scope.
• Acceptance criteria: no unrelated customer records appear in output.

4. Schema validation test ```json { "answer": "string", "confidence": 0, "needs_human_review": false, "sources": [] } ``` Acceptance criteria: invalid JSON never reaches the app UI; it falls back safely instead.

5. Mobile UX error-state test

• Kill one upstream module in Make.com during testing.
• Acceptance criteria: app shows a clear retry state within 2 seconds and does not freeze on spinner screens.

6. Load and latency check

• Run 20 consecutive requests through staging.
• Acceptance criteria: p95 response time stays under 3 seconds for normal questions; failure rate stays below 2 percent.

7. Permission check

• Confirm production scenario credentials cannot read admin-only Airtable views.
• Acceptance criteria: least-privilege access verified by account role review.

8. Human escalation test

• Trigger a low-confidence query intentionally.

Acceptance criteria: system routes to manual review instead of inventing an answer.

Prevention

If I were putting guardrails around this product long term, I would use five controls:

• Monitoring
• Track failed scenarios, schema mismatches, high-risk prompts, and low-confidence responses daily.
• Set alerts when failure count exceeds 5 per hour or when response quality drops below target thresholds.

• Code review discipline
• Review prompt changes like production code changes because they affect behavior directly.

-,Every prompt edit should have an owner,,a reason,,and a rollback plan.,

• Security boundaries

-,Keep secrets out of Airtable.,Use environment variables,,connection managers,,and least privilege on every account., -,Separate public content from operational data.,Never let user text become instruction text without sanitation.,

• UX guardrails

-,Show when an answer is AI-generated.,Show loading,,error,,and retry states.,Give users a way to report wrong answers fast., -,If confidence is low,,say so plainly rather than pretending certainty.,

• Performance controls

-,Cache stable reference data where possible.,Avoid sending huge records into every request.,Keep third-party scripts off critical mobile paths., -,A slower flow increases retries,,which increases cost,,confusion,,and support load.,

I also recommend keeping a small evaluation set of at least 30 real prompts:

• 10 normal questions
• 10 edge cases
• 10 adversarial injection attempts

That set becomes your smoke test every time someone touches prompts, Airtable structure, or Make.com routing logic.

When to Use Launch Ready

Launch Ready fits when you need this stabilized fast without turning it into a multi-week cleanup project. I handle domain, email, Cloudflare, SSL, deployment, secrets, and monitoring so your production surface is clean before you keep iterating on AI logic.

What is included:

• DNS setup
• Redirects and subdomains
• Cloudflare configuration
• SSL setup
• Caching and DDoS protection
• SPF/DKIM/DMARC email records
• Production deployment checks
• Environment variables and secrets handling
• Uptime monitoring
• Handover checklist

What you should prepare before I start:

• Access to your domain registrar
• Cloudflare account access if already created
• Mobile app repo or build access
• Make.com scenario access
• Airtable base access plus list of safe views
• Current production URL(s)
• Any known failing examples with screenshots or logs

If your issue is unreliable answers plus prompt injection risk, I would pair Launch Ready with a focused security hardening sprint next so we can tighten prompt design, validation, and monitoring after deployment cleanup is done. That gives you a safer launch path than patching symptoms one by one while traffic keeps hitting production.

Delivery Map

References

• https://roadmap.sh/api-security-best-practices
• https://roadmap.sh/ai-red-teaming
• https://roadmap.sh/qa
• https://developers.airtable.com/
• https://www.make.com/en/help

---

Take the next step

If this is a problem in your product right now, here is what to do next:

• [Use the free Cyprian tools](/tools) - estimate cost, score app risk, check launch readiness, or pick the right service sprint.

• [Book a discovery call](/contact) - I will tell you honestly whether you need a sprint or if you can DIY the next step.

*Written by Cyprian Tinashe Aarons - senior full-stack and AI engineer helping founders rescue, launch, automate, and scale AI-built products.*