AI Support Lab

1. What is an API?

API stands for Application Programming Interface. It is a defined way for one software application to communicate with another.

Think of a restaurant. A customer places an order, a waiter carries it to the kitchen, and the kitchen prepares a meal. The waiter brings the result back to the customer. An API plays a similar communication role between software systems.

A simple AI request journey

Visitor
  │  "Please explain my report."
  ▼
Website or App
  │  Prepares the request
  ▼
API Endpoint
  │  Sends the request to the AI service
  ▼
AI Service
  │  Processes the request
  ▼
API Response
  │  Returns a result or an error
  ▼
Website or App
  │  Displays the result
  ▼
Visitor

Remember: An API is not the AI model itself. It is the interface through which a website or application requests a service.

2. What is an API request?

A request is a message one application sends to another, asking it to perform an action or return information.

For example, a website might send this simplified request body:

{
  "model": "example-model",
  "message": "Explain this Excel report"
}

• model indicates which AI model should be used.
• message contains the user's question or instruction.

Real APIs also specify information such as a destination endpoint, HTTP method, headers, and sometimes an authentication credential. The example above is illustrative, not a copy-and-paste request for a particular provider.

What is an endpoint?

An endpoint is a specific address where an API accepts requests. For example, an application might send chat requests to a path such as /api/chat.

3. What is an API response?

A response is what the service sends back after receiving a request.

A simplified successful response might look like this:

{
  "answer": "Your sales increased this month."
}

A response may also include a status code, error details, or other metadata. An HTTP success response means the technical request succeeded; it does not guarantee that an AI-generated answer is factually correct.

4. What is an API key?

An API key is a credential used to identify or authenticate access to a service, depending on how the provider implements it.

You can think of it as an access pass. If a required credential is missing, invalid, or no longer valid, a request may be rejected.

Security note: Never place real API keys in public repositories, screenshots, client-side code, support tickets, or shared logs. Use your application's secure secret-management method.

5. What are logs?

Logs are records of events that happen inside a software system. They help support teams reconstruct what happened and when.

For example:

10:30:01  Request received
10:30:02  Connecting to AI service
10:30:03  HTTP 401 Unauthorized
10:30:03  Request failed

This tells us that a request reached the application, an AI service was contacted, and the service rejected the request with an authentication-related status. It does not, by itself, prove exactly which credential or configuration is responsible.

6. Common HTTP status codes

An HTTP status code helps describe the outcome of a web request. These are useful starting points for investigation, not complete diagnoses.

Example: Understanding a 401 error

10:30:01  Chat request sent
10:30:02  HTTP 401 Unauthorized
10:30:02  Message: Invalid API key

What we know: The request failed during authentication.
What we investigate: Whether the application is using the correct credential and authorization settings.
What we avoid: Sharing the secret key or declaring the root cause confirmed before checking it.

7. What information should you look for in logs?

Example log

Time:       10:30:02
Request ID: req-4721
Endpoint:   /api/chat
Status:     429
Duration:   85 ms
Message:    Rate limit exceeded

A useful initial support summary would be:

“The chat request was rejected with an HTTP 429 rate-limit response at 10:30. The request ID is req-4721. We should review account limits, recent request volume, and recommended retry behavior.”

This is more helpful than saying only, “The AI is broken.”

8. Step 1 — Understand the customer's problem

Suppose a customer says:

“My AI assistant is not responding.”

Start by asking focused questions:

• When did the problem begin?
• Does it affect one user or several users?
• What message appears on screen?
• Was the same feature working before?
• Did any settings or permissions recently change?

Goal: Define the impact and scope before looking for a solution.

9. Step 2 — Reproduce the issue

Reproducing means safely trying to observe the same problem again in an approved testing environment.

For example, if a customer reports that a file upload fails, try uploading a safe sample file using a test account with appropriate permissions.

• If the same issue appears, you have a repeatable test case.
• If it does not appear, investigate differences in account settings, file type, size, permissions, or browser environment.

Goal: Turn a general complaint into something you can observe and describe.

Privacy note: Do not reuse sensitive customer files or private data without authorization. Use test data whenever possible.

10. Step 3 — Inspect relevant logs

Suppose the log shows:

Upload started
File validation completed
HTTP 413 Content Too Large
Upload rejected

HTTP 413 indicates that the request content exceeds a limit set by the server or another component handling the request.

The useful next step is to check the application's upload limits and the file's size, rather than changing unrelated settings.

Goal: Use evidence to narrow the investigation.

11. Step 4 — Identify the likely cause

Connect the customer's experience with the available evidence.

• 401: Investigate authentication and credentials.
• 403: Investigate account permissions.
• 429: Investigate rate limits and usage.
• 503: Investigate service availability.

Avoid claiming certainty without enough evidence.

Good professional wording:

“The current evidence suggests that requests may be reaching a rate limit. We are checking account usage and service settings before confirming the cause.”

Goal: Separate a probable explanation from a verified root cause.

12. Step 5 — Escalate with useful evidence

Escalation means handing an issue to another person or team with the access or expertise needed to resolve it.

Instead of writing “AI does not work,” provide a structured ticket.

Example technical support ticket

Goal: Help the technical team continue the investigation without starting again from zero.

13. Step 6 — Communicate clearly with the customer

The customer does not need every technical detail. They need to know what is happening and what the next step will be.

Example customer update

“We found that your requests are being limited by the service. Our technical team is reviewing the account settings. We will share another update once we have confirmed the cause and the appropriate next steps.”

Good communication should be:

• Clear: Avoid unnecessary technical jargon.
• Accurate: State what is known and what remains under investigation.
• Calm: Explain the process without creating unnecessary concern.
• Responsible: Do not promise a fix or resolution time that has not been confirmed.

Goal: Build trust while the technical investigation continues.