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Using llms.txt can help an AI coding assistant find the right API documentation, but it cannot make payment logic safe by itself. In a case study for the Interswitch Developer Community, Gabriel R Mogaji describes using Interswitch’s documentation index while adding Quickteller value-added services (VAS) to Reton, a digital wallet for African users. The central engineering lesson is to use documentation retrieval to guide implementation while keeping wallet balances, ledger changes, and payment decisions under application control.
Why the integration needed better documentation discovery
Mogaji describes Reton as a wallet with a double-entry ledger and protected transfers. The planned VAS features included airtime, data, electricity, and television payments through Interswitch Quickteller. While using an AI coding tool, he needed to direct it to documentation for the right product surface rather than let it conflate bills payment with airtime e-pins or send-money services.
That distinction matters because an API name or a familiar payment pattern is not enough to establish the correct endpoint, request fields, or transaction behavior. The case study’s approach is to narrow the assistant’s reference material to the documentation that applies to the task.
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How llms.txt fits into an AI-assisted API workflow
The case study describes Interswitch’s llms.txt as a plain-text catalogue of documentation titles, links, and brief descriptions. It also says that appending .md to a documentation URL returns the page as Markdown. In this workflow, the index is a discovery tool: it helps identify relevant pages, which can then be supplied to the coding assistant in a more focused form than a large catalogue or browser-oriented HTML.
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- Start with the index. Find documentation entries relevant to the specific service, such as bills payment, rather than asking the assistant to infer the product from a broad API name.
- Choose only task-relevant pages. Select the references needed for the particular integration step, and provide those links or Markdown contents to the assistant.
- Trace implementation details to authoritative pages. Check endpoint behavior, required fields, response states, and merchant configuration against the current vendor documentation before relying on generated code.
- Keep business rules outside the prompt. Define which application components may alter balances and how transaction outcomes are handled; documentation retrieval does not replace those controls.
This is a practical retrieval method, not proof that the generated implementation is correct. The account does not establish that every current Interswitch documentation page supports the described Markdown behavior or that the case study’s implementation matches current API requirements.
Keep the wallet ledger separate from the payment rail
In Mogaji’s architecture, Reton owns the customer experience, wallet balances, ledger entries, and recovery behavior. Interswitch supplies the payment rail and VAS services underneath. The described bill-payment flow keeps provider HTTP calls behind a gateway, while the ledger service remains the only component allowed to change wallet balances.
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This boundary prevents an external payment response—or AI-generated code—from becoming an unrestricted mechanism for changing customer funds. The provider can report a payment outcome; the wallet application must decide how that outcome affects its own ledger under defined rules.
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Payment controls in the described flow
The case study outlines a server-side flow in which checks occur before the wallet is debited and the provider is called. These are the controls the author says Reton’s process uses; they should not be read as an independently audited description of a production system.
- Validate the customer and request. Check authentication, idempotency, any configured PIN requirement, KYC limits, and fraud controls before initiating payment.
- Debit through the ledger service. Reduce available wallet funds through the component authorized to change balances, rather than allowing the provider-call layer to make ledger changes directly.
- Call the provider from the server. Keep payment-provider HTTP requests on the application server, behind the gateway.
- Record confirmed outcomes. On approval, record the successful payment. On a clear decline, reverse the debit according to the application’s ledger rules.
- Resolve uncertain outcomes safely. If the request times out or remains pending, keep it unresolved and seek server-side confirmation. Do not treat a client redirect or query parameter as proof of success.
The distinction between a clear decline and an ambiguous result is critical: reversing a debit after a timeout could conflict with a payment that the provider actually processed, while marking an unconfirmed transaction successful could credit or fulfill an order incorrectly. The account recommends confirmation from the server side for unresolved outcomes.
Do not guess configuration or inquiry-dependent values
Mogaji warns that biller payment codes vary by merchant and environment, and that placeholder values shown for Reton are not production codes. They should not be copied into a live integration. Obtain the applicable codes and configuration through the authoritative vendor process for the merchant and environment in use.
The case study also identifies customer validation and inquiry-defined amounts for some electricity and television payments as gaps in the implementation it describes. Where an amount depends on a bill inquiry, the integration should not assume a fixed value or fabricate one in generated code. Confirm the current product requirements and the source of the payable amount before enabling that payment flow.
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The account is useful as an implementation pattern: use a documentation index to find specific references, keep AI-generated work bounded by those references, and maintain clear ownership of balances and transaction controls. It does not provide an independently verified production audit, a measured improvement in development speed, or evidence that a documentation index alone prevents incorrect API behavior. The case study page is reported as published September 30, but its year is not established here, so its implementation details should be checked against current vendor documentation before use.
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For developers evaluating this workflow, the useful questions are whether the index is machine-readable, whether pages are available in clean Markdown, whether a page identifies the exact product and version, and whether implementation-critical fields can be traced to authoritative documentation. These are practical checks, not comparative benchmark results.
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