How to make LLMs deterministic, in plain English. The version I share with founders and product teams before they make decisions worth real money.
You use AI tools every day. But can you explain what happens when you hit send?
Most people cannot. And that gap is costing them. Bad prompts. Broken products. Decisions made on the wrong assumptions.
The Hard Truth
Every LLM explainer out there is written for researchers or so basic it tells you nothing useful. Neither helps you build better products or work with AI more effectively.
This is the version I share with senior leaders, founders, and product teams before they make decisions worth real money.
1. It Is Not a Search Engine. It Is Not a Database. It Is a Prediction Machine.
When you type a prompt and hit send, the LLM is not finding an answer from somewhere. It is predicting the most likely words to follow your input. Based on patterns it learned from billions of documents.
That is the whole process.
Wrong: "The AI knows the answer."
Right: "The AI predicts the most likely answer based on what it has seen."
This changes everything about how you use it. When an AI gives you a wrong answer confidently, it is not broken. It is doing exactly what it was built to do. Predict. Not verify.
2. The Autocomplete Comparison (And Why It Only Gets You Halfway)
You have probably heard the phrase "autocomplete on steroids." It is not wrong. But it misses something important.
Your phone autocomplete learned from your messages. An LLM learned from most of the written internet. Books. Research papers. Code. Billions of examples.
At that scale, the patterns start to look a lot like real thinking. Not because the model understands in the way you do. Because it has seen so much that it can predict what a good answer looks like.
When I was building AstroNayak I fed Vedic astrology principles into the system prompt. The LLM produced interpretations that genuinely surprised me. It did not know Vedic astrology. It had seen enough of it to predict what a good interpretation would sound like. In practice, that is very useful.
3. The Same Question Can Give You Different Answers
Here is something most people never realise. An LLM is not deterministic. That means you can ask it the exact same question twice and get two different answers.
This is by design. When the model predicts the next word, it is not always picking the single most likely one. It often picks from a range of likely options, with a little randomness added. That randomness is what makes the writing feel natural instead of robotic.
But it has a real cost when you are building products.
Wrong: "I tested it once and it worked, so it will always work."
Right: "It gave a good answer once. I need to test it many times to trust it."
This is why you cannot test an AI feature the way you test normal software. Normal code gives the same output every time. An LLM does not. If your product breaks when the answer comes out slightly different, you have a problem you need to design around from day one.
I fixed this for AstroNayak readings. Here is how.
Most AI astrology tools ask an LLM to guess your chart. AstroNayak does not as the Vedic astrology rules are deterministically coded in the backend, so the AI only interprets what the engine has already calculated. No hallucinated planets. No invented predictions. That is where you constrain a model and change it from probabilistic to deterministic.
4. Context Window: The Most Misunderstood Idea in AI
The context window is everything the model can see at one time. Think of it as short term memory, not long term memory. When a conversation goes past the limit, earlier parts disappear completely.
This is why:
- LLMs forget things you said earlier in long conversations
- You need to give the model your documents directly if you want it to use them
- Bigger context windows cost more because every word gets processed
Wrong: "The AI should remember what I told it last week."
Right: "Memory does not exist by default. It is something I have to build in."
Every AI product that broke because "it forgot the instructions" is a context window problem. Not a model problem.
5. Why It Makes Things Up (And Why That Will Not Change)
Everyone calls it hallucination. A better way to think about it is this. The model made a confident prediction without real facts to back it up.
The model is built to produce clear smooth text. It has no built in signal that says "I do not know this." So when you ask about something it has not seen enough of, it produces the most likely sounding answer anyway.
The fix is not a better model. The fix is how you build around it.
- Give the model the facts you need it to use
- Ask for structured outputs so it cannot wander away from the answer
- Add checks that catch wrong answers before users see them
This is the difference between a prototype and a real product. A prototype trusts the model. A real product does not.
6. What This Means When You Are Building With AI
Five questions I ask before any AI product decision:
- What happens when the prediction is wrong? Plan for it now.
- What happens when the same question gives a different answer? Test for it. Evals, evals, evals.
- What does the model need in front of it to give a good answer? Put that there.
- Am I asking it to find something or reason through something? These need different approaches.
- Have I defined what a good output looks like? Clear formats reduce almost every failure.
AstroNayak works because I stopped treating the LLM as a mystery box and started treating it as a very capable prediction engine that needs good inputs and clear guardrails.
The Takeaway
LLMs are prediction engines. Not knowledge stores. And they will not give you the same answer twice. Build with that understanding and half your AI product problems disappear before you write a line of code.
Next issue: RAG explained simply. Why every serious AI product uses it and whether you need it.
Ridhika | The AIPM Lab | astronayak.com
Top comments (0)