AI as a Universal Information Interface

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The advent of LLMs allows us to get access to vast quantities of information that may or may not be correct. It’s up to us to sort through that information to make sure that it’s been verified for accuracy. But the information it provides is mostly correct. The chance that it is correct improves if the generated text does not require much context or reasoning.

One use case for AI that requires very little context at all is returning structured data, as if the LLM were an API. As long as a human is able to review the output for accuracy, that means that a completion API is able to stand in for just about any read-only API out there.

Unlike specialized APIs that perform one function, a completion API can act as a universal interface to a wide range of tasks and services—effectively serving as a stand-in for many other specialized APIs.

1. LLMs have already absorbed LOTS of unstructured web data: These models have been trained on massive datasets scraped from the internet, including technical documentation, web apps, APIs, and natural language descriptions. This means they have latent knowledge of how thousands of APIs and domains work, even without direct integration.
2. LLMs can dynamically create structured output: Unlike fixed-function APIs, a Completions API can generate outputs in a variety of structured formats—JSON, XML, Markdown, SQL, HTML, even proprietary data schemas—based solely on the prompt.
3. A single API integration is enough to create a POC: You don’t need to implement a different client, token, or SDK for each new API. Instead, you pass natural-language or programmatic prompts to a single endpoint, and get intelligent responses across domains. In this case, the sum is greater than its parts, because new, interdisciplinary information can be returned.

By way of example,

Limitations to Keep in Mind

• Lack of live data: Without tools or plugins, the model can’t fetch real-time information. It only simulates knowledge based on training data and inference.
• No persistent state: It doesn’t “remember” past interactions unless context is preserved by the user or application.
• No guaranteed accuracy: It can hallucinate or make up answers if unsure, especially when simulating APIs or databases.

Overcoming those limitations - AI as the Universal Adapter

A completion API can function as a wrapper or front-end to other bespoke APIs.

⚠ Note

This is now called a Tool, such as those that are defined by MCP.

Tools that are given to LLMs can be called by the LLM, as long as it formats the call correctly. The rest of the article describes tools which are able to make API calls.

The LLM would convert a natural language query into a REST call, execute it, then format the response back to natural language.

Example

A simple natural language query like “How many orders did we ship to Germany last week?” could be transformed into a structured API call like this:

User Input (Natural Language)
→ "How many orders did we ship to Germany last week?"

Completions API (NLP Translation Layer)
→ Parses and interprets the intent, entities, and timeframes.
→ Outputs a structured API call:

{
  "endpoint": "/orders",
  "method": "GET",
  "params": {
    "country": "Germany",
    "date_range": "2025-04-28 to 2025-05-04"
  }
}

Backend API (Your Custom Logic)
→ Executes the call and returns raw data.

Completions API (Response Interpretation)
→ Converts structured API response into natural language:
"We shipped 153 orders to Germany between April 28 and May 4."

Benefits of This Wrapper Approach

• Abstracts complexity: Users don’t need to know endpoints, parameters, or schema details.
• Multimodal I/O: Can return structured data, visualizations, or summaries depending on the request context.
• Rapid iteration: Business logic can evolve while maintaining a consistent user-facing interface.
• Domain adaptation: With fine-tuning or prompt engineering, the model can learn your domain-specific vocabulary or API structure.

Example Use Cases

Technical Implementation Hints

Use the Completions API to output structured JSON (via schema-based prompting or function-calling syntax).

Use a middleware layer that:

• Validates and sanitizes model outputs.
• Maps model outputs to concrete API calls.
• Feeds results back through the model for summary or user-facing presentation.

Bonus: Augmented Interaction

You can also support follow-up queries (like “What about France?”) by keeping conversational context and allowing the model to generate delta queries or comparisons automatically.