# How the Prompt-Settings System Customizes AI Instructions in ThePrimeagen's 99
> Discover how the prompt-settings system customizes AI instructions in ThePrimeagen's 99. Learn about its Lua pipeline, XML-like tags, and extensible fragments for tailored AI operations.
- Repository: [ThePrimeagen/99](https://github.com/theprimeagen/99)
- Tags: how-to-guide
- Published: 2026-02-16
---
**The prompt-settings system in 99 centralizes AI instruction customization through a Lua-based pipeline that wraps user prompts with XML-like tags, injects contextual metadata, and allows extensible prompt fragments for different operations.**
ThePrimeagen's `99` repository implements a sophisticated prompt-settings architecture that controls how every LLM request is constructed and contextualized. Understanding how this prompt-settings system customizes AI instructions reveals the mechanism behind the plugin's ability to generate tutorials, perform semantic searches, and modify code with precise contextual awareness.
## The Prompt-Settings Pipeline Architecture
The system operates as a five-stage pipeline where raw user instructions are transformed into fully contextualized AI prompts. Each stage is implemented in specific Lua modules within the `lua/99/` directory, ensuring that every LLM request follows a consistent structure while remaining customizable.
## Step 1: Prompt Definitions in prompt-settings.lua
At the core lies [`lua/99/prompt-settings.lua`](https://github.com/ThePrimeagen/99/blob/main/lua/99/prompt-settings.lua), which exports a `prompts` table containing static fragments and dynamic formatting functions.
Static fragments include:
- `prompts.role` – Defines the AI as a "software-engineering assistant"
- `prompts.tutorial()` – Returns instructions for creating tutorials
- `prompts.semantic_search()` – Specifies strict output formats for search results
Dynamic helpers include:
- `prompts.prompt(user_prompt, action, name?)` – Wraps prompts in XML-like tags
- `prompts.visual_selection()` – Formats selected code blocks
- `prompts.output_file()` and `prompts.read_tmp()` – Handle file system references
## Step 2: Prompt Composition with make-prompt.lua
When an operation executes, [`lua/99/ops/make-prompt.lua`](https://github.com/ThePrimeagen/99/blob/main/lua/99/ops/make-prompt.lua) orchestrates the composition. The `make_prompt()` function calls `context._99.prompts.prompts.prompt()`, which constructs a wrapped prompt structure:
```lua
user prompt
operation-specific prompt
```
This XML-like envelope separates user intent from operational context, allowing the LLM to distinguish between direct instructions and background requirements.
## Step 3: Context Metadata Injection
Before transmission, `RequestContext:finalize()` in [`lua/99/request-context.lua`](https://github.com/ThePrimeagen/99/blob/main/lua/99/request-context.lua) appends three critical metadata blocks:
- `` – The file path and position where changes apply
- `` – The selected code range or function text
- `...` – Constraints telling the model exactly where it may write changes
This injection ensures the AI understands both the spatial context (where am I?) and the file system boundaries (what can I touch?).
## Step 4: Operation-Specific Prompts
Each operation in `lua/99/ops/` imports specific prompt fragments:
- [`tutorial.lua`](https://github.com/ThePrimeagen/99/blob/main/tutorial.lua) uses `prompts.tutorial()` to request educational content generation
- [`search.lua`](https://github.com/ThePrimeagen/99/blob/main/search.lua) uses `prompts.semantic_search()` to enforce strict location-string formatting for results
These specialized prompts extend the base system without modifying the core pipeline.
## Step 5: Extending the System
The architecture supports custom prompts through simple table extension. To add a new prompt:
```lua
-- In lua/99/prompt-settings.lua
prompts.my_special = function()
return [[
You are asked to generate a summary of the given file.
The output must be a single paragraph.
]]
end
```
Then reference it in any operation:
```lua
local special_prompt = ctx._99.prompts.prompts.my_special()
local full_prompt = ctx._99.prompts.prompts.prompt(
"Summarize the current buffer", special_prompt, "SUMMARY"
)
```
No other code changes are required, making the system fully extensible.
## Practical Code Examples
### Using a Built-in Prompt
```lua
local ctx = _99.State:from_current_buffer(1)
ctx:finalize()
local prompt = ctx._99.prompts.prompts.tutorial()
print(prompt) -- Returns the full "create a tutorial" prompt string
```
### Composing a Full Request
```lua
local make_prompt = require("99.ops.make-prompt")
local full_prompt, refs = make_prompt(
ctx,
ctx._99.prompts.prompts.semantic_search(),
{ additional_prompt = "Find all usages of `foo`", additional_rules = {"my_rule"} }
)
```
### Viewing the Final Meta-Prompt
```lua
ctx:finalize()
local meta = table.concat(ctx.ai_context, "\n")
print(meta) -- Complete prompt sent to the model including Location, FunctionText, etc.
```
## Summary
- The **prompt-settings system** centralizes all AI instruction customization in [`lua/99/prompt-settings.lua`](https://github.com/ThePrimeagen/99/blob/main/lua/99/prompt-settings.lua)
- **XML-like tags** (``, ``, ``, ``, ``) structure the prompt into semantic sections
- **Context metadata** is injected by `RequestContext:finalize()` in [`lua/99/request-context.lua`](https://github.com/ThePrimeagen/99/blob/main/lua/99/request-context.lua), providing spatial awareness and file-system constraints
- **Operation-specific prompts** (tutorial, semantic_search) extend the base system without pipeline modifications
- **Extensibility** is achieved by adding new functions to the `prompts` table, requiring no changes to core composition logic
## Frequently Asked Questions
### How does the prompt-settings system wrap user instructions?
The system uses the `prompts.prompt()` function in [`lua/99/prompt-settings.lua`](https://github.com/ThePrimeagen/99/blob/main/lua/99/prompt-settings.lua) to wrap user instructions in XML-like tags. It creates a structure with `` tags containing the user prompt and `` tags containing the operation-specific instructions, allowing the LLM to distinguish between direct user intent and background context.
### What metadata does RequestContext:finalize() add to AI prompts?
`RequestContext:finalize()` in [`lua/99/request-context.lua`](https://github.com/ThePrimeagen/99/blob/main/lua/99/request-context.lua) appends three critical metadata blocks: `` specifying the file path and cursor position, `` containing the selected code range or function, and `...` defining the temporary file where the model must write changes. This ensures the AI understands both spatial context and file system constraints.
### Can I add custom prompts without modifying core files?
Yes, you can extend the system by adding new functions to the `prompts` table in [`lua/99/prompt-settings.lua`](https://github.com/ThePrimeagen/99/blob/main/lua/99/prompt-settings.lua). Simply define a new function like `prompts.my_custom = function() return [[your instructions]] end`, then reference it in any operation via `ctx._99.prompts.prompts.my_custom()`. The composition pipeline in [`make-prompt.lua`](https://github.com/ThePrimeagen/99/blob/main/make-prompt.lua) automatically handles the new prompt without requiring changes to core logic.
### Where are operation-specific prompts like tutorial and semantic_search defined?
Operation-specific prompts are defined as functions in the `prompts` table within [`lua/99/prompt-settings.lua`](https://github.com/ThePrimeagen/99/blob/main/lua/99/prompt-settings.lua). For example, `prompts.tutorial()` returns instructions for creating educational content, while `prompts.semantic_search()` specifies strict formatting requirements for search results. These are then imported by their respective operation files in `lua/99/ops/` such as [`tutorial.lua`](https://github.com/ThePrimeagen/99/blob/main/tutorial.lua) and [`search.lua`](https://github.com/ThePrimeagen/99/blob/main/search.lua).