Building Production-Ready LLM Applications: Beyond the Prototype

The demo worked. The prototype impressed everyone. Now what?

If you've built an LLM-powered application, you've probably experienced this moment: your proof-of-concept works beautifully in development, but the path to production feels unclear. You're not alone. The gap between a working demo and a reliable production system is where most LLM projects stall.

This post covers the key challenges and patterns we've learned deploying LLM applications at scale.

The Production Gap

Building an LLM prototype is deceptively easy. A few API calls, some prompt engineering, and you have something that feels magical. But production demands more:

Let's tackle each of these.

1. Design for Non-Determinism

LLMs don't behave like traditional APIs. Accept this early.

Structured outputs are your friend. Instead of parsing free-form text, use function calling or JSON mode to get predictable response formats. This makes downstream processing reliable.

# Instead of parsing "The answer is 42" from free text
response = client.chat.completions.create(
    model="gpt-4",
    messages=[...],
    response_format={"type": "json_object"}
)

Add validation layers. Treat LLM outputs like untrusted user input. Validate, sanitize, and have fallback behaviors when responses don't meet expectations.

Retry with backoff. Transient failures happen. Build retry logic with exponential backoff into your LLM calls from day one.

from tenacity import retry, stop_after_attempt, wait_exponential
 
@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=4, max=10)
)
async def call_llm(prompt: str) -> str:
    response = await client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}],
        timeout=30
    )
    return response.choices[0].message.content

2. Architect for Latency

Users expect responsiveness. A few strategies that help:

Stream responses. Don't wait for the full response before showing something to the user. Streaming creates the perception of speed even when total time is the same.

Cache aggressively. Many queries are semantically similar. Use embedding-based caching to serve similar questions from cache:

Query → Embed → Check cache → Hit? Return cached : Call LLM

Choose the right model for the task. Not every request needs GPT-4. Use smaller, faster models for classification, routing, and simple tasks. Reserve expensive models for complex reasoning.

Parallelize when possible. If your pipeline has independent steps, run them concurrently. A chain of five sequential LLM calls is slow; five parallel calls followed by aggregation is fast.

3. Build an Evaluation System Early

This is where most teams struggle. How do you know if your LLM application is actually working?

Define metrics before you build. What does "good" look like? Accuracy? Relevance? Factual correctness? Define it, then measure it.

Create a golden dataset. Maintain a set of input-output pairs that represent expected behavior. Run your system against this dataset on every change.

LLM-as-judge works (with caveats). Using one LLM to evaluate another is practical and scales well. Just be aware of biases — LLMs tend to prefer verbose responses and may miss subtle errors.

4. Implement Guardrails

Production systems need boundaries.

Input validation. Filter prompt injection attempts, check for PII, enforce length limits. Don't pass raw user input directly to your prompts.

Output filtering. Check responses for sensitive information, off-topic content, or harmful outputs before returning them to users.

Rate limiting and quotas. Protect your system (and your budget) from abuse. Implement per-user and global rate limits.

5. Observability is Non-Negotiable

You can't improve what you can't measure.

Log everything. Inputs, outputs, latencies, token counts, model versions. You'll need this data for debugging and optimization.

Trace complex chains. When using agents or multi-step pipelines, implement distributed tracing. Know which step failed and why.

Monitor for drift. Model behavior changes over time (especially with hosted APIs). Track quality metrics continuously, not just at deployment.

6. Manage Costs Proactively

LLM costs can surprise you. Stay ahead of them:

• Track token usage per feature. Know where your spend is going.
• Optimize prompts. Shorter prompts with few-shot examples often outperform long ones.
• Use tiered models. Route simple queries to cheaper models.
• Set budgets and alerts. Know before you get the bill.

The Path Forward

Building production LLM applications is genuinely hard — but it's hard in ways that are becoming well-understood. The patterns above aren't theoretical; they're battle-tested approaches from teams shipping real products.

At Pelles, we build AI tools that help construction teams work smarter. If you're curious how we can help your team, let's talk.