What Actually Happens When You Call an LLM API

You send a prompt. You get a response. It feels almost instant — a second or two, maybe less. But between your code and that first streamed token, your request passes through multiple layers of infrastructure, gets transformed, routed, processed, and shaped before any “thinking” even begins.

Most explanations of how LLM APIs work jump straight to inference — attention mechanisms, transformers, token generation. That’s like explaining how a car works by starting at the engine. What about the ignition? The fuel system? The road you chose to drive on?

As a developer building on these APIs, I kept noticing something: the parts I actually control matter more to my output quality than the parts I don’t. The model’s behavior isn’t just decided during inference. It’s shaped earlier — by prompt assembly, context selection, and tool availability. And those are exactly the parts most diagrams skip.

I’m not building inference engines. I’m building on top of them. This is the mental model I wish I had when I started.

Let’s walk through all seven stages, grouped into four phases: what you control, what the provider handles, what the model does, and what happens after.

Phase 1: Context and Control

Section 1 — Input and Context

“Everything the model knows comes from here.”

The API call doesn’t start at the API. It starts in your code.

Before your request ever hits a server, there’s a critical step that most tutorials gloss over: prompt assembly. This is where you construct the model’s entire reality — everything it will know and everything it won’t.

A typical API call includes a messages array. Tutorials show you something like:

{
  "messages": [{ "role": "user", "content": "Hello" }]
}

And that’s technically correct. It works. But in production, that messages array is assembled from multiple sources:

• System instructions — the behavioral rules you set for the model. How it should respond, what role it plays, what constraints it follows.
• User message — the actual input from the person or event that triggered the call.
• Conversation history — previous turns in the conversation, if you’re building something multi-turn. You decide how much history to include.
• Retrieved context — results from a vector database, a knowledge base, or a RAG (Retrieval Augmented Generation) pipeline. This is how you give the model information it wasn’t trained on.
• Memory — if your application maintains any form of persistent memory across sessions, it gets injected here.
• Tool definitions — if the model can call functions, search the web, or interact with external services, those capabilities are defined in this request.

Every one of these components is optional except the user message. But each one you include (or exclude) shapes the model’s behavior. The context window isn’t just a size limit — it’s a budget. What you choose to spend it on is an engineering decision.

This is the highest-leverage stage in the entire pipeline. A carefully assembled context can make a mediocre model produce great output. A sloppy context can make the best model produce nonsense. The assembly logic is the product.

Phase 2: Infrastructure

Section 2 — Infrastructure Entry

“Your request is validated and routed before any AI runs.”

Once your assembled request leaves your code, it hits the provider’s API gateway. This is standard web infrastructure — the same kind of gateway that sits in front of any large-scale API.

Here, your request gets authenticated (is your API key valid?), and rate limits are enforced (have you exceeded your allowed requests per minute?). If you’ve ever received a 429 Too Many Requests error, that’s this layer doing its job.

After authentication, a load balancer routes your request to available compute resources. Providers run large GPU clusters, and the load balancer decides which cluster or instance handles your specific call. This is why identical prompts can return with slightly different latency — the routing path may differ each time.

None of this is unique to LLMs. It’s the same infrastructure pattern behind any cloud API at scale. But it’s worth knowing it exists, because when things go wrong (timeouts, rate limits, inconsistent latency), the problem often lives here, not in the model itself.

Section 3 — Preparation

“Text becomes numbers. The system decides which model handles it.”

Before inference can begin, your text needs to be converted into something the model can process. This is tokenization — your prompt gets broken down into token IDs, which are numerical representations of words, subwords, or characters.

Different providers use different tokenizers (you may have heard of BPE, SentencePiece, or similar approaches), and the exact way text gets split into tokens varies. This matters for one very practical reason: token count determines your cost. Every provider’s pricing page bills by the number of input and output tokens.

After tokenization, there’s another layer that’s rarely discussed publicly: model routing. If a provider offers multiple model sizes, or runs different versions of a model across their infrastructure, something needs to decide which specific model instance handles your request. The details of how providers route requests internally aren’t publicly documented, and I won’t pretend to know exactly how any specific provider does it. But knowing this layer exists helps explain why you might occasionally see slightly different behavior or response times on the same prompt and model.

Phase 3: Inference

Section 4 — The Inference Engine

“This is where the model actually ‘thinks.’”

This is the stage everyone wants to understand. It’s also the stage you influence least directly.

Inference happens in two phases:

The prefill phase processes all your input tokens. The model reads your entire assembled context — system prompt, user message, conversation history, tool definitions, all of it — and builds an internal representation. This is commonly referred to as the KV cache (key-value cache). The longer your prompt, the more work happens here, and the longer you wait before seeing the first output token. This is why long system prompts and large context windows have a real cost beyond just token pricing — they increase your time-to-first-token.

The decode loop is where output gets generated, one token at a time. Each new token is informed by everything that came before it — both your input and the tokens the model has already generated. This is the autoregressive loop, and it’s why LLM responses stream in token by token rather than appearing all at once. Streaming exists because of this architecture: each token can be sent to you as it’s generated, giving the user the experience of watching the response unfold in real time.

The sampling parameters you set in your API call — temperature, top_p, top_k — influence how the model picks each next token during this loop. Lower temperature means more predictable output; higher means more creative variation. These parameters are well-documented in every provider’s API reference.

This phase is where most of your wait time goes. But here’s the thing: you don’t change how attention works. You don’t redesign the decode loop. Your leverage as a developer is in what you send the model (Phase 1) and what you do with its output (Phase 4). The inference engine does its thing. Your job is to set it up for success.

Phase 4: Output and Loop

Section 5 — The Tool Loop

“The model may pause, use tools, and continue reasoning.”

This is the section most LLM pipeline diagrams skip entirely. And in 2025, it might be the most important one to understand.

When you include tool definitions in your API request (Phase 1), you’re giving the model the ability to do something other than generate text. It can choose to call a function, run a web search, query a database, or interact with an external service through protocols like MCP (Model Context Protocol).

When the model decides to use a tool, here’s what happens:

1. Instead of generating a final text response, the model outputs a tool call — a structured request saying “I want to call this function with these arguments.”
2. Your code (or the provider’s infrastructure) executes the tool and gets a result.
3. The tool result gets appended to the context.
4. Inference runs again — the model now has the original prompt plus the tool result, and it continues generating from there.

This is the agentic loop. One user prompt can trigger multiple inference passes. The model might search the web, read the results, decide it needs more information, search again, and then finally compose a response. Each loop means more tokens processed, more latency, and more cost.

If you’re building anything with tool use — and increasingly, most production LLM applications are — understanding this loop is essential. It’s the difference between a straightforward API call and a complex chain of operations that can run up your token bill quickly. I learned this the expensive way.

Section 6 — Post-Processing

“Output is cleaned and validated before returning.”

Once the model finishes generating (either because it reached a natural stopping point, hit your max token limit, or completed a tool loop), the raw output tokens go through post-processing.

The tokens get converted back to readable text (detokenization). Safety classifiers and content moderation systems evaluate the output — every major provider runs these. If you’ve ever received a response that was blocked or flagged by a content filter, that’s this stage at work.

For structured output requests (like JSON mode), validation happens here too. The output gets checked against the expected format.

There’s a small but useful detail that lives in this stage: the finish_reason field in your API response. It tells you why generation stopped:

• stop — the model reached a natural ending
• length — it hit your max token limit
• content_filter — the output was blocked by safety systems
• tool_use — the model wants to call a tool (back to Section 5)

This field is your diagnostic tool. If your responses keep getting cut off, check the finish reason before assuming the model is broken.

Section 7 — Response and Metrics

“Every token is measured, logged, and billed.”

Your response arrives either as streaming chunks (Server-Sent Events) or as a complete JSON response, depending on how you configured the request.

Every response includes usage metadata: how many tokens were in your prompt, how many tokens were generated, and the total. This is your billing data. Input and output tokens are priced separately, with output tokens typically costing more — check your provider’s current pricing page for exact rates, as these change.

On the provider side, everything gets logged: which model handled the request, token counts, latency, finish reason, and safety flags. This feeds their monitoring dashboards, abuse detection systems, and capacity planning.

Where Your Leverage Actually Is

If you take one thing from this article, let it be this:

An LLM API call is a pipeline of context shaping → inference → optional tool loops → output processing, wrapped in infrastructure that routes and measures every token.

Every stage matters, but they’re not equally within your control. Your real leverage as a developer is in three places:

1. What you put in the context — you control this completely, and it has the highest impact on output quality.
2. How you design tool loops — you decide what tools the model can use and how results flow back into the conversation.
3. How you parse and validate output — you own error handling, retries, and what happens when things don’t come back as expected.

The inference engine is impressive. But it’s the part you influence least. Your craft as a developer building on LLMs lives in everything around inference.

Next time your API call returns something unexpected, don’t start by blaming the model. Trace the pipeline. Start with what you sent it.

Most diagrams show inference. But behavior is decided earlier — in prompt assembly, context selection, and tool availability. That’s the inside scoop.

Carmelyne is a Full Stack Developer and AI Systems Architect. She is a previously US-bound Filipino solopreneur, former Media and Information Literacy and Empowerment Technologies instructor, and the author of Thinking Modes: How to Think With AI — The Missing Skill.