> For the complete documentation index, see [llms.txt](https://university.obvious.in/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://university.obvious.in/working-with-features/building-with-ai/understand-the-tech.md).

# Understand the tech

{% hint style="info" %}
**Assumed audience:** You’re a product manager working on an LLM powered feature. You’re familiar with how LLMs work. Use this as a starting point to think about the problems that LLMs are best suited to solve, and to start experimenting with these solutions.
{% endhint %}

## 👋 Introduction

Large Language Models provide a way for computers to understand natural human language and respond meaningfully. This was not possible prior to 2022.

***

## 1️⃣ Understand the tech

> Think of LLMs as small reasoning engines rather than as a magical black box to throw stuff into. The small reasoning engine approach makes it reliable, reduce hallucinations and reduce cost of operation as well.

{% embed url="<https://youtu.be/VXkDaDDJjoA?T=2941>" %}

> Try not to misuse the LLM - like don’t answer someone’s birthday using an LLM just because you can. Using an LLM for it is less reliable and more expensive than querying a database.

{% embed url="<https://www.youtube.com/watch?v=WqYBx2gB6vA>" %}

***

## 2️⃣ Further reading

{% embed url="<https://www.promptingguide.ai/>" fullWidth="false" %}

{% embed url="<https://platform.openai.com/docs/guides/fine-tuning>" %}

{% embed url="<https://maggieappleton.com/squish-structure>" %}

{% embed url="<https://research.ibm.com/blog/retrieval-augmented-generation-RAG>" %}

***


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://university.obvious.in/working-with-features/building-with-ai/understand-the-tech.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
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Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.