How to Scale LLM Applications in Production
Building a simple ChatGPT-based chat prototype is an evening project. Just a few lines of Python code, an API key and... it works. But the moment you want to deploy this solution in a company or make it available to thousands of users is when the fun ends and engineering begins.
Integrating a Large Language Model (LLM) into existing products requires an entirely new architecture. We move from a simple script to managing complex state, data pipelines, and model unpredictability.
In this post, I'll explain how to make the journey from "works on my machine" to stable production using a modern tech stack.
1. Why Is This So Hard? Engineering Challenges
Before we get to solutions, we need to understand why the classic backend approach isn't enough here. LLM applications differ from typical systems in several critical ways:
• Statelessness: The model operates "here and now". After completing a query, it immediately forgets what you talked about. The burden of managing conversation history and context falls on your side.
• Context Limits: You can't just paste your entire SQL database into the prompt. Models have limited "capacity", so you must precisely select only the information that's relevant at any given moment.
• Non-determinism: In classic code, 2 + 2 always equals 4. In LLMs, the same prompt can give different answers. This is a nightmare for application testing.
• Logic Complexity: Simple chatbots work linearly (Question -> Answer). Modern systems (called Agents) must operate in loops, make decisions, backtrack and correct errors – which regular APIs can't handle.
2. Your New Stack: Tools and Orchestration
To tame this chaos, we don't write everything manually. In the LLM world, it's common to separate roles into Tools and Orchestration.
A. LangChain – Your "Librarian"
LangChain has become an industry standard. Think of it as an advanced toolbox and interface to your data.
• Role: It serves to communicate with models (OpenAI, Anthropic, HuggingFace), but its superpower is data management.
• Librarian Function: LangChain has ready-made modules (Loaders, Splitters) that can "digest" your PDF files, web pages or SQL databases, split them into chunks and prepare them for search.
• Flexibility: Thanks to abstraction, changing a model from GPT-4 to Claude 3 is often a matter of changing one line of code.
B. LangGraph – "The Brain of Operations"
While LangChain provides the building blocks, LangGraph decides how to use them. It's an orchestrator that acts like a project manager.
• Why is it needed? A regular Chain works like a simple production line: A -> B -> C. LangGraph introduces a graph structure: A -> B -> (Check result) -> If wrong, go back to A / If good, go to C.
• State Memory: LangGraph knows what stage the process is at (e.g., "I fetched data, but haven't summarized it yet").
• Example: A user asks a difficult question. LangGraph decides: "I need to search the web". It fetches data (using LangChain). Evaluates the result: "This isn't enough". Decides to search again (loop). Only when satisfied does it generate a response.
3. RAG Architecture (Retrieval-Augmented Generation)
Even the best model doesn't know your company's data. This is where RAG comes in – the heart of systems based on private knowledge.
Thanks to the combination of LangChain and LangGraph, the RAG process becomes "Agentic" (Agentic RAG):
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Ingestion: We fetch documents, split them and store them in a vector database (LangChain rules here).
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Retrieval (Search): In response to a user's question, the system extracts key fragments.
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Verification and Generation: If the found fragments are of poor quality, the "Brain" (LangGraph) can decide to rephrase the question and search again, instead of "hallucinating" an answer.
4. Infrastructure: The Foundation Under Your Application
When we move out of the prototype phase, we need to ensure stability ("high availability").
Load Balancing and Key Management
Models have their limits (Rate Limits). If your application becomes popular, one API key won't be enough.
You need a system (like a hotel reception) that distributes traffic. If the main model is overloaded or "down", the system must automatically switch to a backup key or another, cheaper model (e.g., from GPT-4 to GPT-3.5-turbo).
LLMOps: CI/CD and Evaluation
This is the biggest mental shift compared to classic IT. How do you know that after changing the prompt, your bot works better?
The solution is building a test dataset (e.g., 100 pairs of questions and reference answers). Before each deployment in the CI/CD pipeline, an automated process runs that passes these questions through the new version of the bot and evaluates the quality of responses. Without this, you're operating blindly.