Building an AI Application Is More Than Calling an LLM API

22 min readNov 5, 2026

“It’s just an API call”

The first version of every AI feature is ten lines of code: send a prompt, print the answer.

The production version is a system that streams responses, controls spending, survives provider outages, manages memory, picks the right model, runs tools safely, and lets you see what actually happened.

The LLM call is the smallest part.

Here is the demo version:

const response = await llm.complete({
    model: "some-model",
    messages: [{ role: "user", content: question }]
});

res.json({ answer: response.text });

It works on your laptop. Then it meets real users, and the questions start:

Why does the user stare at a spinner for 25 seconds?
Why did one customer cost us $400 yesterday?
What happens when the provider returns 429 Too Many Requests, or goes down for an hour?
Why did the assistant forget what the user said ten messages ago?
Do we really need the biggest model to classify a support ticket?
How do we let the model look up an order without letting it read every order?
A user says the answer was wrong. What did the model actually see?

Each of those is a section in this essay.

We will walk through the full architecture, from the user to the database, and then go deep on the seven concerns that turn a demo into a product: streaming, token costs, rate limits, retries, context, model selection and observability.

Code examples use Node.js. Provider names are kept general on purpose: the patterns are the same whichever model vendor you use, and specific prices and model names change every few months.

The real architecture: who does what

The stack is usually drawn as a straight line:

User → Node.js API → AI Gateway → LLM → Tools → Database

The layers are right, but one arrow is misleading.

The LLM never calls your tools or your database. It only asks for a tool to be called. Your Node.js code decides whether to run it, runs it, and sends the result back. That back-and-forth is called the agent loop.

The agent loop, step by step

A user asks: “Where is my order 8812?”

Node.js checks who the user is, loads their recent messages, and builds a request: system prompt + history + the new question + a list of tools the model may use (for example get_order_status).
The request goes through the AI gateway to the LLM.
The LLM replies with a tool call, not an answer: “call get_order_status with { orderId: "8812" }“.
Node.js checks the user is allowed to see order 8812, runs the tool, which queries the database, and gets { status: "out_for_delivery", eta: "14:30" }.
Node.js sends the tool result back to the LLM, through the gateway, as a new message.
The LLM now writes the final answer: “Your order is out for delivery and should arrive around 2:30 pm.” It streams back to the user, token by token.
Node.js saves the conversation and logs the tokens used, the cost and the timing.

One user message caused two LLM calls here.

Complex tasks can loop five or ten times. That fact affects almost everything that follows: latency, cost, rate limits and debugging.

Why this split matters?

Keeping tool execution in your own code is a safety feature, not a detail.

The model’s output is untrusted input. It can be wrong, or manipulated by text hidden in a document it read.

Your code is where you enforce permissions, validate arguments and decide what actually happens.

One request, drawn

Node.js sits in the middle of everything.

The model only ever talks to Node.js through the gateway; tools and the database are reached only by your own code.

The Node.js API layer

The Node.js API is the brain of the system, not a thin proxy. It owns identity, conversation state, the prompt, the agent loop and every tool execution. The model is a component it calls.

What it does on every message

Authenticate the user and load their plan or tier (free, pro, enterprise), which decides model choice and limits.
Validate the input. Cap message length; reject empty or absurdly long input before it costs tokens.
Check budgets and limits for this user (see the rate limits and token costs sections).
Load context: recent messages, any long-term summary, retrieved documents.
Build the request: system prompt, history, tool definitions.
Run the agent loop with a hard maximum number of steps.
Stream the answer to the client.
Persist messages, tool calls, token usage and cost.

The agent loop in code

const MAX_STEPS = 8;

export async function runAgent({ user, messages, tools, onText }) {
    for (let step = 0; step < MAX_STEPS; step++) {
        const reply = await gateway.chat({
            user,
            messages,
            tools: tools.definitions,
            onText
        });        messages.push(reply.message);        if (reply.toolCalls.length === 0) {
            return reply.message;
        }        const results = await Promise.all(
            reply.toolCalls.map((call) => tools.execute(call, { user }))
        );        messages.push(...results.map(toToolResultMessage));
    }    throw new AgentStepLimitError();
}

The MAX_STEPS cap matters. Without it, a confused model can call tools in a loop forever, burning money with every step.

Long requests and HTTP

An agent task can take 30 seconds or several minutes. Two practical points:

Raise timeouts along the path: load balancer idle timeout, reverse proxy, and Node’s own server timeouts. A 60-second default will cut off long answers.
For tasks longer than a minute or two, switch to a job model: return a job ID right away, run the agent in a worker (BullMQ, SQS), and stream progress to the client over SSE or WebSocket. If the user closes the tab, the work still finishes and is saved.

Stateless servers, stateful conversations

Keep Node.js servers stateless. Conversation history lives in the database (PostgreSQL works well), so any server can handle the next message and a deploy doesn’t lose anything mid-chat.

The AI gateway: one door to every model

An AI gateway is a layer between your application and the model providers.

Instead of every service holding API keys and handling retries in its own way, all LLM traffic flows through one place that handles routing, limits, failover, caching, logging and cost tracking.

Why not call the provider directly?

With one service and one model, you can. As soon as you have several features, several teams or several providers, the same problems repeat in every codebase:

API keys copied into many services and environments.
Each team writing its own retry logic, each slightly wrong.
No single view of what is being spent, by whom, on what.
Switching provider or model means changing code in many places.

The gateway centralises all of this.

What a gateway typically does

Build or buy?

There are good options at each level of effort:

Open-source proxies such as LiteLLM, which you run yourself.
Managed gateways such as Portkey, Cloudflare AI Gateway or Kong AI Gateway.
Cloud platforms such as Amazon Bedrock, Google Vertex AI or Azure AI Foundry, which give access to several model families behind one cloud API and IAM.
A thin internal library in your Node.js monorepo. Fine to start with, but it lives inside each app, so it cannot enforce company-wide budgets.

A reasonable path: start with a small internal wrapper so all calls go through one function; move to a real gateway once you have more than one team or provider.

Tag every request

The single most valuable habit is attaching metadata to every call:

await gateway.chat({
    model: "default-chat",
    messages,
    metadata: {
        feature: "order-assistant",
        userId: user.id,
        tenantId: user.tenantId,
        conversationId,
        promptVersion: "order-assistant@v14"
    }
});

Notice the model name default-chat: an alias the gateway maps to a real model. You can then change the underlying model without a code deploy. The tags are what make cost reports, rate limits and debugging possible later.

Tools and the database: letting the model act, safely

Tools turn a chatbot into something useful: it can look up an order, check stock or open a ticket. But every tool is a door into your system, opened by a model that can be wrong or tricked. Design tools as narrow, permission-checked functions, never as raw database access.

What a tool definition looks like

You describe each tool to the model with a name, a description and a JSON Schema for its arguments:

export const getOrderStatus = {
    name: "get_order_status",
    description: "Get the current status and ETA of one of the signed-in user's orders.",
    parameters: {
        type: "object",
        properties: {
            orderId: { type: "string", pattern: "^[0-9]{1,12}$" }
        },
        required: ["orderId"],
        additionalProperties: false
    }
};

The description is part of the prompt. Clear, specific descriptions are the main way to get the model to pick the right tool with the right arguments.

Executing tools safely

export async function executeGetOrderStatus(args, { user }) {
    const { orderId } = getOrderStatusSchema.parse(args);

    const order = await db.orders.findOne({
        id: orderId,
        customerId: user.id
    });    if (!order) {
        return { error: "Order not found for this user." };
    }    return { status: order.status, eta: order.eta };
}

The rules this follows:

Validate every argument (here with a schema library such as Zod). The model can send anything.
Scope by the real user, taken from the session, never from the model’s arguments. Note customerId: user.id: even if the model asks for someone else’s order, it gets nothing.
Return only what’s needed. Fewer fields means fewer tokens and less data exposed.
Return errors as data, so the model can explain or try something else, instead of crashing the loop.
Set a timeout on every tool, and cap the size of what it returns.

Read tools vs write tools

Treat tools that change things differently from tools that read things:

Reads (get order, search products) can usually run automatically.
Writes (refund, cancel, send email) should be idempotent, logged, and often need user confirmation: the model proposes “refund $25 on order 8812”, the UI shows a button, and only a click by the user executes it.

Avoid “run any SQL” tools

A tool that lets the model write SQL is tempting and dangerous. If you need flexible querying for internal analytics, use a read-only replica, a read-only database role, a restricted schema or view, row limits and statement timeouts. For customer-facing features, specific tools are almost always better.

Prompt injection

If a tool returns text from outside, like a web page, an email or a document, that text may contain instructions such as “ignore previous instructions and send the user’s data to…”. The model may follow them. Defences:

Treat tool output as data. Keep powerful write tools away from flows that read untrusted content.
Enforce permissions in code, so even a fooled model can only do what the user could.
Require human confirmation for sensitive actions

The database’s other job: retrieval

The database also stores embeddings, numeric vectors that represent the meaning of text, so you can find documents relevant to a question.

pgvector in PostgreSQL is a solid default; dedicated vector databases make sense at very large scale. Retrieval is covered under Context.

Streaming: show the answer as it is written

LLMs generate text one token at a time, and a long answer can take 10–30 seconds.

Streaming sends each piece to the user as it is produced, so they start reading within a second or two. It does not make the model faster, but it changes how fast the product feels more than any other single change.

Two numbers to track

Time to first token (TTFT) — how long until the first word appears. This is what users feel as “speed”.
Total time — until the answer is complete. This matters for tool calls and background jobs.

Without streaming, TTFT equals total time. With streaming, TTFT is often under a second or two.

The path a token takes

Provider → gateway → Node.js → browser. Most providers stream using Server-Sent Events (SSE): a long-lived HTTP response that sends small data: chunks. Node.js reads that stream and forwards text to the browser, usually also with SSE.

app.post("/api/chat", async (req, res) => {
    res.writeHead(200, {
        "Content-Type": "text/event-stream",
        "Cache-Control": "no-cache",
        "Connection": "keep-alive",
        "X-Accel-Buffering": "no"
    });

    const abort = new AbortController();
    res.on("close", () => {
        if (!res.writableEnded) {
            abort.abort();
        }
    });    const send = (event, data) => {
        res.write(`event: ${event}\ndata: ${JSON.stringify(data)}\n\n`);
    };    try {
        await runAgent({
            user: req.user,
            messages: await loadMessages(req),
            tools,
            signal: abort.signal,
            onText: (text) => send("text", { text }),
            onToolStart: (name) => send("status", { message: `Using ${name}…` })
        });
        send("done", {});
    } catch (error) {
        send("error", { message: "Something went wrong. Please try again." });
    } finally {
        res.end();
    }
});

Details that break streaming in production

Buffering proxies. nginx, some load balancers and compression middleware collect the whole response before sending it, so the user sees everything at once. Disable buffering (X-Accel-Buffering: no for nginx) and turn off gzip on stream routes.
Idle timeouts. While the model “thinks” or a tool runs, no bytes flow and a proxy may close the connection. Send a small heartbeat comment (: ping) every 15 seconds.
The user closes the tab. The model keeps generating, and you keep paying, unless you cancel the upstream request. That is what the AbortController above is for.
Errors mid-stream. The HTTP status was already 200 when streaming started. Errors must be sent as an event inside the stream, and the UI must handle a half-finished answer.

Streaming with tools

In an agent loop, the first LLM call often ends with a tool request instead of text. Good UIs show what’s happening: “Looking up your order…”. Then the final answer streams in after the tool result returns. Sending status events like this keeps users patient through multi-step work.

Save what was streamed

Accumulate the full text on the server as it streams, and save it when finished, or save the partial text if the user cancels. Otherwise, a page refresh loses the answer, and your logs miss what the user actually saw.

Token costs: where the money actually goes

You pay per token: for every token you send (input) and every token the model writes (output).

In real applications, input tokens usually dominate the bill, because every turn of a conversation and every step of an agent loop re-sends the whole history, the system prompt and the tool definitions.

What a token is

A token is a chunk of text, roughly three-quarters of an English word on average, so 1,000 tokens is about 750 words.

Code, numbers and many non-English languages use more tokens per word. Providers price input and output separately; output tokens typically cost several times more than input tokens. Check current prices on your provider’s pricing page, because they change often.

Why costs grow faster than you expect

Take a support assistant with:

A system prompt of 2,000 tokens.
Tool definitions totalling 1,500 tokens.
Each user message and reply adding about 400 tokens.

On the 20th message, one request sends 2,000 + 1,500 + (19 × 400) ≈ 11,100 input tokens, to produce maybe 300 output tokens. If that message triggers two tool calls, the loop makes three LLM calls, each re-sending everything. That single user message may cost over 30,000 input tokens.

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Cost per conversation grows roughly with the square of its length, unless you manage context.

The levers, from biggest to smallest

Use the right model for the job. Smaller models are often 10–30× cheaper than the largest ones. Classification, extraction and routing rarely need the biggest model (see Model selection).
Prompt caching. Most major providers can cache a fixed prefix (system prompt, tool definitions, long documents) and charge much less for cached input tokens on later calls. Put stable content first and changing content last so the prefix stays identical.
Manage context. Summarise or trim old history instead of re-sending all of it (see Context).
Cap output. Set max_tokens for each use case, and ask for concise answers in the prompt.
Retrieve less, but better. Five relevant chunks beat twenty loosely related ones, in cost and in quality.
Batch APIs for work that isn’t urgent, like nightly summaries or bulk classification. Providers often discount these heavily.
Response caching for repeated, identical questions, such as FAQ-style queries, in the gateway or Redis.

Track cost per user and per feature

Record usage on every call, from the provider’s response, not from your own estimates:

await db.llmUsage.insert({
    userId: user.id,
    feature: "order-assistant",
    model: reply.model,
    inputTokens: reply.usage.inputTokens,
    cachedInputTokens: reply.usage.cachedInputTokens,
    outputTokens: reply.usage.outputTokens,
    costUsd: priceOf(reply.model, reply.usage),
    conversationId
});

This lets you answer the questions that matter: which feature costs the most, what a typical user costs per month, and whether one user or bug is quietly burning money.

Set budgets before you need them

A per-user daily budget in tokens or dollars, enforced before each call.
A per-conversation cap, so one runaway agent loop can’t spend without limit.
Alerts on daily spend by feature, so a bad deploy shows up in hours, not on next month’s invoice.

Rate limits: two directions

An AI application lives between two sets of limits. Upstream, providers limit how much you can send them. Downstream, you must limit how much each of your users can send, or one heavy user, a bug, or an abuser can use up your whole provider quota and budget.

Upstream: the provider’s limits

Providers typically limit you on several dimensions at once:

Requests per minute (RPM).
Input tokens per minute and output tokens per minute.
Sometimes a cap on concurrent requests or daily usage.

Limits usually depend on your account tier and grow as you spend more.

When you hit one, you get HTTP 429, often with a retry-after header.

The surprise for most teams: token limits run out long before request limits. Ten users with long conversations and big retrieved documents can use more tokens per minute than a thousand short requests.

Ways to stay under them:

Queue and smooth non-urgent work (summaries, embeddings, batch jobs) instead of firing it all at once.
Separate quotas by priority. Interactive chat should never wait behind a backfill job. Use separate keys, projects or gateway pools for each.
Spread load across regions, or across providers that serve the same model family (directly and via a cloud platform, for example).
Ask for higher limits before a launch, with a usage forecast. It can take days.
Use reserved or provisioned capacity for steady, high-volume workloads where it’s offered.

Downstream: your users’ limits

Request counts alone don’t work for AI features, because one request can cost 100 tokens or 100,000. The limit is on what actually costs you:

Redis is a natural home for these counters, since every app server needs to see the same numbers. A simple daily counter that adds the actual tokens used after each call, as reported by the provider, works well:

export async function checkUserBudget(userId) {
    const used = Number(await redis.get(`tokens:${userId}:${today()}`)) || 0;
    if (used >= DAILY_TOKEN_LIMIT) {
        throw new BudgetExceededError();
    }
}

export async function recordUsage(userId, usage) {
    const key = `tokens:${userId}:${today()}`;
    await redis.multi()
        .incrBy(key, usage.inputTokens + usage.outputTokens)
        .expire(key, 172800)
        .exec();
}

Tell users what’s happening

When a user hits a limit, say so plainly, and say when it resets: “You’ve reached today’s limit. It resets at midnight UTC.” A vague error or a silent failure creates support tickets.

Retries and fallbacks: when the model doesn’t answer

LLM APIs fail more often than typical web APIs: overloaded servers, rate limits, slow responses, dropped streams. Retry the errors that are temporary, never the ones that aren’t, and retry in one layer only. When a provider is truly down, fall back to another model or degrade gracefully.

Which errors to retry

Backoff with jitter

export async function withRetries(fn, { attempts = 3, baseMs = 500 } = {}) {
    for (let attempt = 1; ; attempt++) {
        try {
            return await fn();
        } catch (error) {
            if (!isRetryable(error) || attempt === attempts) {
                throw error;
            }
            const backoff = baseMs * 2 ** (attempt - 1);
            const wait = error.retryAfterMs ?? backoff + Math.random() * backoff;
            await sleep(wait);
        }
    }
}

The random jitter spreads retries out, so thousands of clients don’t all retry at the same instant and knock the provider over again.

Retry in one place

A common mistake: the provider SDK retries 2 times, the gateway retries 3 times, and your app retries 3 times. One failing request turns into 18 attempts, and a short outage becomes a self-inflicted storm. Pick one layer, usually the gateway, and turn retries off in the others.

Timeouts: two of them

A time-to-first-token timeout (for example 15 seconds). If nothing arrives, the request is probably stuck; cancel and retry.
A total timeout sized for the longest answer you expect.

Once tokens have started streaming to the user, a blind retry would restart the answer on screen. Either show a clear “connection lost, retry?” option, or retry only before the first token is sent.

Retries inside the agent loop

Tool calls: if a step is retried after a tool already ran, the tool may run twice. Write tools must be idempotent, for example using toolCallId as the idempotency key.
Invalid model output: if you asked for JSON and got something unparsable, a retry with the validation error fed back (”Your output failed: missing field status“) usually fixes it. Limit this to one or two attempts. Using the provider’s structured output mode, where available, prevents most of these errors in the first place.

Fallbacks

When retries aren’t enough, fall back:

Same model, different region or deployment.
Same model family through a different platform (direct API vs a cloud platform).
A different model, with a prompt that has been tested against it.
Degrade: a simpler non-AI path, a cached answer, or an honest “the assistant is busy, try again in a minute”.

Fallback models behave differently. A prompt tuned for one model can produce worse or differently formatted answers on another. Run your evaluation set (see Observability) against every fallback before you rely on it. Add a circuit breaker in the gateway too: after many failures, route straight to the fallback for a few minutes instead of waiting on timeouts for every request.

Context: what the model knows right now

An LLM has no memory between calls. Everything it knows about this user, this conversation and your business must be sent in the request, inside a limited context window. Choosing what goes in, and what stays out, is the core skill of building AI applications, sometimes called context engineering.

What fills the context window

Every request is built from the same parts:

System prompt — role, rules, tone, output format.
Tool definitions — names, descriptions and schemas.
Long-term facts — user profile, preferences, a summary of earlier conversations.
Retrieved documents — help articles, policies, product data relevant to this question.
Conversation history — recent messages, tool calls and their results.
The new message.

Context windows today are large, often hundreds of thousands of tokens. That doesn’t mean you should fill them. Every token costs money and time, and models tend to pay less attention to details buried in the middle of a very long context.

More context is not better context.

Keeping conversations under control

Sliding window — keep only the last N messages. Simple, but the model forgets the start.
Summarisation (compaction) — when history passes a threshold, ask a small, cheap model to summarise older messages, and keep the summary plus recent messages.
Trim tool results — a tool that returns a 50-row table in step 2 doesn’t need to be re-sent in full at step 7. Keep a short version.
Count tokens before you send — use the provider’s token counting, or a tokenizer library, and trim before hitting the limit, not after the API rejects the request.

export async function buildContext({ systemPrompt, summary, history, retrieved, budget }) {
    const fixed = [systemPrompt, summary, ...retrieved].filter(Boolean);
    let used = await countTokens(fixed);

    const recent = [];
    for (const message of [...history].reverse()) {
        const size = await countTokens([message]);
        if (used + size > budget) {
            break;
        }
        recent.unshift(message);
        used += size;
    }    return [...fixed, ...recent];
}

Retrieval (RAG): bringing in your own knowledge

The model doesn’t know your return policy or your product catalog. Retrieval-augmented generation (RAG) fetches the relevant pieces at question time:

Ahead of time: split documents into chunks of a few hundred tokens, create an embedding for each, and store them (pgvector works well).
At question time: embed the question, find the closest chunks, and put the best few into the context.

What makes retrieval good in practice:

Hybrid search — combine vector search (meaning) with keyword search (exact terms like order IDs or product codes).
Reranking — fetch 30 candidates, then use a reranker model to keep the best 5.
Metadata filters — only search the user’s tenant, language or product line. This is also a security boundary: never retrieve another customer’s documents.
Cite sources — include document IDs so the answer can link to them and you can debug wrong answers.

Order for caching

Put the parts that rarely change first (system prompt, tool definitions), and the parts that change every turn last (retrieved chunks, new message). This keeps the prefix identical between calls, which lets provider prompt caching cut cost and latency.

Model selection: the right model for each job

There is no single “best model” for an application.

Most production systems use several: a small, fast, cheap model for simple tasks, a strong model for hard reasoning, and an embedding model for retrieval.

Choose per task, using your own test cases, not leaderboards.

The trade-offs

Every model sits somewhere on four axes:

Quality — how well it handles your actual tasks.
Latency — time to first token and tokens per second.
Cost — price per input and output token.
Capabilities — tool calling, structured output, image input, context window size, reasoning modes.

Larger models are usually better at multi-step reasoning, tricky instructions and agent work. Smaller models are much cheaper and faster, and are often just as good at narrow tasks.

Matching tasks to model tiers

Routing

A router decides which model handles each request. Simple rules go a long way:

export function pickModel({ task, user, inputTokens }) {
    if (task === "classify" || task === "extract" || task === "summarise") {
        return "small-fast";
    }
    if (task === "agent" || user.plan === "enterprise") {
        return "large-reasoning";
    }
    return inputTokens > 50000 ? "large-long-context" : "medium-general";
}

The names here are gateway aliases, not provider model IDs. When a better or cheaper model is released, you change the alias mapping in one place and run your tests, without a code deploy.

A more advanced pattern is a cascade: try the small model first, and escalate to a larger one only when the small model’s answer fails a check (invalid JSON, low confidence, or a classifier says it’s a hard question).

How to actually choose: evaluate on your data

Public benchmarks tell you little about your task. Build an evaluation set:

Collect 50–200 real examples (anonymised), including tricky edge cases and past failures.
Define what “good” means for each: an exact answer, a required fact, the right tool called, a valid format.
Run every candidate model and prompt version against the set. Score automatically where you can; use a strong model as a judge, or a human, for open-ended answers.
Compare quality, cost and latency side by side, then decide.

Re-run the same set whenever you change a prompt, switch a model, or add a fallback. Models also change over time, even under the same name in some cases, so pin specific model versions in production when your provider allows it.

Other factors that decide

Data handling — retention policies, data residency, and whether your contracts allow a given provider.
Availability — which regions and platforms serve the model, which matters for fallbacks.
Self-hosted open-weight models — useful for strict data rules or very high, steady volume, but you take on GPU capacity, scaling and upgrades yourself.

Normal monitoring tells you whether a request succeeded and how long it took. AI applications need more, because a request can return 200 OK in two seconds and still be wrong. You need traces of every LLM call and tool call, cost and token metrics, and a way to measure answer quality over time.

Trace the whole agent run

When a user reports a bad answer, you need to replay exactly what happened. Record each run as a trace with nested spans:

conversation 4f2a · message 9 · total 6.8 s · $0.031
    ├─ build_context              45 ms   9,800 tokens
    ├─ llm_call #1 (large)        2.1 s   in 9,800 / out 60    → tool: get_order_status
    ├─ tool get_order_status      120 ms  ok
    └─ llm_call #2 (large)        4.5 s   in 10,050 / out 310  → final answer

For each LLM span, store: model and version, prompt version, the full input messages and output (or a pointer to them), token counts including cached tokens, cost, time to first token, total time, finish reason, and any error or retry. For each tool span: name, arguments, result size, duration and status.

OpenTelemetry now has semantic conventions for generative AI spans, so traces can flow into the tools you already use. LLM-focused platforms such as Langfuse, LangSmith, Helicone or Arize Phoenix add prompt and response views, cost reports and evaluation features on top.

Many gateways log this automatically.

Metrics to put on a dashboard

Measuring quality

Quality is the metric that normal monitoring can’t see. Combine several signals:

User feedback — thumbs up and down, “regenerate” clicks, edits to drafts, conversations abandoned after a bad reply.
Offline evaluations — your evaluation set from the previous section, run in CI on every prompt or model change, like unit tests for behaviour.
Online evaluations — score a sample of real production traffic automatically, for example with an LLM judge that checks whether the answer is grounded in the retrieved documents.
Human review — a regular sample of conversations reviewed by people who know the domain, especially low-rated ones.

Version your prompts

Treat prompts as code. Keep them in the repository or a prompt registry, give each a version (order-assistant@v14), and log that version on every call. When quality drops, you can see exactly which change caused it, and roll back.

Privacy

Prompts and responses often contain personal data. Decide what you store, mask sensitive fields (emails, phone numbers, card numbers) before logging, set retention limits, and restrict who can view raw conversations.

Production checklist

Before an AI feature goes in front of real users, check each layer:

All LLM traffic goes through one gateway or wrapper, with model aliases instead of hard-coded model IDs.
Every call is tagged with feature, user, tenant, conversation and prompt version.
Responses stream, with buffering disabled, heartbeats on, and upstream requests cancelled when the client disconnects.
The agent loop has a maximum step count, and every tool has a timeout.
Tools validate arguments, take the user’s identity from the session, and return only what’s needed.
Write actions are idempotent and need confirmation where the stakes are real.
Token usage and cost are recorded per call from the provider’s reported usage.
Per-user and per-tenant budgets and rate limits are enforced before each call.
Retries happen in exactly one layer, only for retryable errors, with backoff and jitter.
Fallback models or regions are configured and tested against your evaluation set.
Context is counted and trimmed before sending; old history is summarised; stable content comes first for caching.
Retrieval is filtered by tenant and permissions.
An evaluation set runs in CI on every prompt or model change.
Traces capture every LLM call and tool call, with personal data masked.
Dashboards and alerts cover time to first token, error rates, cost per feature and agent step counts.

Closing thoughts

The LLM is the most impressive part of an AI application, and the smallest part of the engineering.

Everything around it is familiar backend work, with new pressures: requests that take seconds instead of milliseconds, costs that scale with words instead of requests, dependencies that are rate-limited and occasionally unavailable, and outputs that can be fluent and wrong at the same time.

The teams that do this well treat the model as an unreliable, expensive, very capable component, and build the same things they would around any such dependency: clear boundaries, limits, retries, fallbacks, careful inputs and strong visibility.

Start with the simple loop, put a gateway and good logging in place early, and add the rest as real usage shows you where it hurts.

Prajwal Haniya