Lumotia/docs/architecture-map/04-llm-formatting-mcp/llm-decompose-task.md

---
name: LLM decompose_task surface
type: architecture-map-page
slice: 04-llm-formatting-mcp
last_verified: 2026/05/09
---

# LLM `decompose_task` surface

> **Where you are:** [Architecture map](../README.md) → [LLM, Formatting, MCP](README.md) → decompose_task

**Plain English summary.** `decompose_task` takes a task description and returns 3 to 7 short imperative micro-steps. The output is a JSON array of strings, constrained at the GBNF level so the model literally cannot emit fewer than three or more than seven. A feedback-conditioned variant adds few-shot examples from the user's HITL corrections.

## At a glance

- Crate: `magnotia-llm`
- Path: `crates/llm/src/lib.rs:254` (`decompose_task`) and `:267` (`decompose_task_with_feedback`)
- LOC: ~40 across both methods
- Public surface:
  - `pub fn decompose_task(&self, task_text: &str) -> Result<Vec<String>, EngineError>`
  - `pub fn decompose_task_with_feedback(&self, task_text: &str, examples: &[prompts::FeedbackExample]) -> Result<Vec<String>, EngineError>`
  - Re-export not exposed at crate root: callers get `prompts::FeedbackExample` via `magnotia_llm::prompts::FeedbackExample` (the `prompts` module is `pub mod prompts;`).
- External deps that matter: GBNF sampler from llama-cpp-2; `serde_json` for the array parse.
- Tauri command that calls this (slice 2, best guess): the only call site is `src-tauri/src/commands/tasks.rs:322` — `engine.decompose_task_with_feedback(&parent_text, &examples)` — invoked from a `decompose_task_*_cmd` (the file's helper name; see slice 2's tasks page when written).

## What's in here

### `decompose_task` (`crates/llm/src/lib.rs:254`)

Convenience wrapper that calls `decompose_task_with_feedback(task_text, &[])`. Behaviour identical to the conditioned variant with no examples.

### `decompose_task_with_feedback` (`crates/llm/src/lib.rs:267`)

Steps:

1. Borrow the loaded model via `loaded_model_arc()`. `EngineError::NotLoaded` if no model is loaded.
2. Build the system prompt: `prompts::build_conditioned_system_prompt(prompts::DECOMPOSE_TASK_SYSTEM, examples)`. With an empty `examples` slice, the base prompt is returned unchanged. With non-empty examples, a few-shot block is appended (see [`llm-prompts-and-grammars.md`](llm-prompts-and-grammars.md) for the rendering rules).
3. Render a chat prompt with two messages — `("system", system)` and `("user", &format!("Task: {task_text}"))`.
4. Call `generate` with:
   - `max_tokens: 512`
   - `temperature: 0.0`
   - `stop_sequences: ["<|im_end|>", "<|im_end_of_text|>"]`
   - `grammar: Some(grammars::TASK_ARRAY_GRAMMAR.to_string())`
5. Parse the raw output via `parse_string_array` (`crates/llm/src/lib.rs:489`): `serde_json::from_str::<Vec<String>>` then trim, drop empties, dedupe case-insensitively while preserving first-seen ordering.

The 3-to-7 lower and upper bound is enforced by the GBNF, not by Rust code. The `parse_string_array` helper is happy to return arrays of any size; `TASK_ARRAY_GRAMMAR` makes that hypothetical impossible.

### Feedback examples

`prompts::FeedbackExample` carries `(input: String, original_output: Option<String>, corrected_output: Option<String>)`. The renderer prefers `corrected_output` over `original_output` so a user's edits beat a thumbs-up on the original. Empty inputs are skipped. Examples without any usable output (no original, no correction) are skipped. See `crates/llm/src/prompts.rs:69` for the renderer and `:93` for the prompt-builder.

## Data flow

```
(task_text: &str, examples: &[FeedbackExample])
  → loaded_model_arc()
  → build_conditioned_system_prompt(DECOMPOSE_TASK_SYSTEM, examples)
       (returns base prompt unchanged when examples is empty)
  → render_chat_prompt([(system, system), (user, "Task: {task_text}")])
  → generate(prompt, GenerationConfig {
       max_tokens: 512, temp: 0.0,
       stops: [<|im_end|>, <|im_end_of_text|>],
       grammar: Some(TASK_ARRAY_GRAMMAR),
     })
  → parse_string_array(raw)
       → serde_json::from_str::<Vec<String>>
       → trim + drop empties + dedupe (case-insensitive, first-seen)
  → Vec<String>
```

## Prompts and grammars

- System prompt: `prompts::DECOMPOSE_TASK_SYSTEM` at `crates/llm/src/prompts.rs:1`. See [`llm-prompts-and-grammars.md`](llm-prompts-and-grammars.md) for the full text.
- GBNF: `grammars::TASK_ARRAY_GRAMMAR` at `crates/llm/src/grammars.rs:16`. Encodes "open bracket, exactly three strings, then up to four optional more strings, then close bracket". Recursive `rest3..rest6` chain is what bounds the array length to 3–7.

## Watch-outs

- **The GBNF is the source of truth for 3–7.** The system prompt also says "between 3 and 7", but the model's only actual constraint is the grammar. If the GBNF is ever loosened (e.g. for a free-text variant), the prompt will silently lose its size guarantee.
- **`parse_string_array` dedupe is case-insensitive.** Two micro-steps that differ only in casing collapse to one. This is desirable for the typical "Buy milk" / "buy milk" failure mode, but a niche prompt that legitimately asks for case variations would lose data.
- **`EngineError::InvalidJson` surfaces malformed grammar output.** In practice the GBNF prevents this, but a `LlamaSampler::grammar` runtime error or a tokenisation edge case can still produce a parse-able-by-llama but not-by-serde string. The error includes the raw output for debugging.
- **Stop sequences fire after detokenisation.** A token boundary that splits `<|im_end|>` is fine — the running buffer accumulates raw bytes via the UTF-8 decoder.
- **`max_tokens: 512` is the array's total budget, not per item.** Seven long imperative sentences will hit the cap. If a real-world task produces output near the ceiling, the JSON will be cut off and `serde_json::from_str` will return `EngineError::InvalidJson`.

## See also

- [LLM engine](llm-engine.md)
- [LLM extract_tasks (sibling array surface)](llm-extract-tasks.md)
- [Prompts and grammars catalogue](llm-prompts-and-grammars.md)
- [Slice README](README.md)