# Technical term (Retrieve)

### Intro

An LLM can follow instructions via prompts. However, an LLM is more sensitive to "technical terms" rather than "general terms". The best way to get precise output is to use technical terms instead of general terms.

### How it work ?

We can change "general terms" into "technical terms" without adjusting other instructions.

````
``` python
def calculate_square_area (x, y) : 
  return x * y
```
Write unit test.
Return only code.
---------------------------------------------------------
``` python
def calculate_square_area (x, y) : 
  return x * y
```
Write boundary test.
Return only code.
````

{% hint style="info" %}
A software developer could consider this ability similar to the **search** functionality in other programming languages.
{% endhint %}

### Prompt example

{% embed url="<https://prompt.float16.cloud/prompt/42addd05-fdf9-40ee-9dd3-b7252349a806>" %}
General term to generate unit test
{% endembed %}

{% embed url="<https://prompt.float16.cloud/prompt/3074c4d8-7615-4e6d-b803-09a156033a72>" %}
Specific term to generate unit test
{% endembed %}

{% embed url="<https://prompt.float16.cloud/prompt/55becc6c-d3fa-4a7d-9ddc-6faa43ecba57>" %}
Specific term to generate unit test
{% endembed %}


---

# Agent Instructions: 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:

```
GET https://docs.float16.cloud/prompting/technical-term-retrieve.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

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.