How to Build Production-Ready LLM Apps: A Straightforward Guide
The AI world is full of buzzwords. Every day, there’s a new term floating around autonomous agents, fine-tuning, prompt engineering. It’s easy to feel like building AI applications is some kind of futuristic sorcery. But let’s cut through the noise. If you’ve ever thought that slapping a GPT API onto an app makes you an AI engineer, this blog will be a wake-up call. Real AI engineering goes beyond just sending prompts and getting fancy responses; it’s about building structured, efficient, and scalable applications that actually solve real problems.
This session, led by Hitesh Bandhu, dives deep into what it really takes to build production-grade AI applications. Hitesh is an AI Engineer and full-stack developer With 2.5 years of experience in AI, he has worked with remote US startups and built his own products, including Hackforge, a landing page generator. His journey in AI has given him firsthand insights into what works and what doesn't when it comes to deploying LLM-powered applications in the real world.
This guide will walk you through everything you need to know about developing production-ready Large Language Model (LLM) applications, from how LLMs work to how companies like Swiggy and Tinder are using them in the real world.
Understanding LLMs: It’s Not Magic, It’s Math
At their core, LLMs are just really advanced "next-word predictors." They analyze an enormous amount of text and try to guess the most probable next word in a sequence. This is possible thanks to an architecture called transformers, which helps the model "pay attention" to different parts of the input text while generating responses.
But how do these models get so smart?
The Two-Stage Training Process
Pre-training: This is like teaching a kid how to speak by making them read everything in a library. The model is trained on massive datasets (think books, articles, Wikipedia, and a suspicious amount of Reddit). It learns how words are structured, what makes a sentence make sense, and even some general knowledge about the world.
Fine-tuning: Once the model knows how to generate text, fine-tuning helps it specialize in specific tasks. This could be anything from answering customer support queries to writing marketing copy or even generating code.
There’s also a difference between base models and instruction-tuned models. A base model (like GPT) is just a smart text generator. But an instruction-tuned model (like ChatGPT) has been trained to follow user commands better. That’s why ChatGPT can understand structured prompts and respond more appropriately than raw GPT.
Calling an API Isn’t AI Engineering (And Other Hard Truths)
If you’ve ever thought, "I built an AI app because I sent a prompt to an API," I’ve got some bad news for you. That’s just the beginning.
Real AI engineering means building a complete system around the LLM. It’s about designing robust applications that handle real-world constraints like scalability, latency, and security.
What True AI Engineers Do
Set Up Guardrails: AI can be unpredictable, so engineers add filters to block inappropriate or misleading responses. This includes profanity filters, bias mitigation, and compliance checks.
Optimize Prompts: Raw outputs from LLMs can be messy. Engineers fine-tune prompts and use techniques like Retrieval-Augmented Generation (RAG) to fetch relevant information before asking the LLM for an answer.
Ensure Accuracy with Citations: If an AI model provides information, it better have some proof to back it up. Companies are now integrating AI-generated citations to boost trust.
Build Reliable Evaluations: AI models need testing, lots of it. Engineers measure how well their system performs under different conditions and refine their approach based on real-world feedback.
Simply put, calling an API is the easiest part of AI engineering. The real work happens in designing, testing, and refining the system that surrounds the model.
What It Really Means to Be an AI Engineer
An AI engineer is like the bridge between machine learning researchers (who build the models) and full-stack developers (who build the applications). They don’t just focus on the AI itself, they care about how it fits into a product.
The AI Engineer’s Mindset
User First, AI Second The goal isn’t to just use AI for the sake of it. The best AI engineers focus on the user experience first. Does the AI actually help solve a problem, or is it just a fancy gimmick?
Choosing the Right Model for the Right Task Every LLM is different. Some are fast and cheap, while others are expensive but highly accurate. AI engineers evaluate models based on cost, performance, and use case.
Mastering the AI Toolbox AI engineers don’t just work with LLMs. They use tools like:
Keeping Up With Research AI evolves fast. The best AI engineers stay updated on the latest advancements and figure out how to integrate them into their systems effectively.
Architecting a Production-Grade LLM App
So, what does a real-world LLM system look like? Let’s break it down.
How to architect a production grade app ?
Caching for Speed Instead of hitting the LLM for every request, AI engineers use caching. If a user asks the same question multiple times, the system can return a saved answer instead of querying the model again.
Retrieval-Augmented Generation (RAG) Instead of relying solely on the LLM’s training data, RAG fetches real-time information from external sources. This is crucial for AI systems that need up-to-date knowledge, like financial data or news summaries.
Guardrails for Safety AI engineers implement input validation (to block bad queries) and output filtering (to prevent harmful responses). This ensures the AI remains safe and compliant.
Choosing the Right Model at the Right Time Some tasks require a large, powerful LLM, while others can be handled by a smaller, cheaper model. A model gateway helps route queries intelligently to optimize costs.
Executing Actions AI applications don’t just answer questions, they can take actions. Whether it’s placing an order, booking a ticket, or retrieving a database record, AI engineers design workflows where the AI actually does something useful.
The Metrics That Matter: Temperature, Top-K, and Top-P
Fine-tuning an LLM’s output isn’t just about better prompts. There are key parameters that control how the model generates responses.
Temperature: A low temperature (e.g., 0.1) makes the model’s answers more focused. A high temperature (e.g., 0.8) makes it more creative.
Top-K Sampling: Limits the number of words the model can choose from at each step, making responses more predictable.
Top-P Sampling: Filters out low-probability words to improve coherence.
Tuning these parameters helps create an AI that behaves the way you want, whether that’s strict accuracy or free-flowing creativity.
The AI Engineer’s Golden Rule: "Know When Not to Use AI"
The first rule of AI engineering? Just because you can use an LLM doesn’t mean you should.
LLMs are great for open-ended tasks but inefficient for structured tasks where a simple database query would be faster. AI engineers must critically assess whether AI adds value, or just adds unnecessary complexity.
Real-World Examples: How Companies Are Using LLMs
Swiggy’s Text-to-SQL Solution Swiggy allows employees to query their database using natural language instead of writing SQL queries. This makes data retrieval easier for non-technical teams.
Tinder’s AI-Powered Safety Features Tinder uses LLMs to analyze messages and detect harmful content, improving trust and safety on the platform.
These companies aren’t just using AI for the sake of it—they’re integrating it in ways that solve real problems.
Q&A Session
Question: What if the government makes their own LLMs from scratch, specific for each domain? Wouldn't that eliminate hallucinations?
Answer: While governments can fine-tune LLMs on their own data, the speaker notes that hallucinations are still a risk. LLMs aren't 100% reliable, especially with numbers. Even a small hallucination rate (e.g., 1%) can lead to significant problems when dealing with large populations, causing reputational and practical issues..
Question: How does the model choose tokens out of the top K samples to autocomplete?
Answer: When a model is trained, it learns which tokens are likely to follow others. This is based on probabilities learned during the pre-training phase. For example, after "He", "is" is a very likely next word. The model doesn't "choose" randomly but uses these probabilities, influenced by the temperature setting. A higher temperature introduces more randomness, allowing lower-probability tokens to be selected.
Question: Is Cursor or Supermaven using the same method to generate code?
Answer: Yes, they largely use the same method to generate code, but they have their own way of parsing the code that comes from the LLMs. They also rely on prompt templates and bounded outputs.
Question: If you have full-stack engineer knowledge and some knowledge of ML and DL, is this enough to be called an AI engineer?
Answer: Not necessarily. While full-stack knowledge is valuable for product development, an AI engineer needs to know how to apply AI models effectively and in a cost-optimised and reliable way. This involves understanding how to use models, not necessarily having deep ML or DL knowledge.
Question: Should I master Langchain to build agents?
Answer: Langchain is widely used, but the speaker suggests exploring alternatives like Fidata, which they find a good intersection between CrewAI and Autogen. Langchain can be complex.
But we have got you covered. Read this article to get started with Langchain.
Question: Why is Perplexity AI so fast?
Answer: Perplexity AI is fast at searching and providing results because they have a pipeline that gives them the power to search the whole internet. They are crawling the web 24/7. Also, Perplexity uses existing LLMs. They also use multiple models and allow the user to select which LLM they want to use.
Question: Let's say you want to build an agent that can book a hotel. How do you design it roughly?
Answer: To design an agent that can book a hotel. First, you need to have a query. Then, like a human, the agent needs to be able to search and interact with websites. Therefore, the agent requires tools for searching (e.g. Google) and interacting with websites (clicking buttons etc.).
Question: What will be the cost of running an AI product with 1,000 to 10,000 users?
Answer: The cost depends on the product type and usage. For a chatbot, the cost will depend on the number of queries. One million requests of 300 tokens each using GPT-4 could cost roughly $3,000.
Question: How can we deploy an agent project in Vercel for live use?
Answer: You need to have a back end. The agentic work happens on the back end. You just need the user to see the answer. If the user needs to see any processing, you can send the logs from the back end to the front end and stream them in real-time.
Question: Are most of the libraries Python? Do I need to understand Python well to build, or can I do a flip?
Answer: If you know Javascript, learning Python will not be a problem. It is beneficial to learn Python. But mostly, if you're using Langraph or Langchain, they are available in Typescript. It won't take more than 15 days to learn Python.
Final Thoughts
Building real-world LLM applications isn’t about playing around with API calls. It’s about designing robust, scalable, and efficient AI systems that serve real users. The best AI engineers know that AI is just a tool : one that needs to be used wisely, efficiently, and with purpose. To deep dive into this session, Check out the full video on TechKareer’s YouTube channel.
Cover Credits : https://engineering.grab.com/supercharging-llm-application-development-with-llm-kit
