# Prose round-trip cycle

## Why

The prose surface (`ail prose`, Iter 20a / 20b) is a deliberately
**lossy** projection of an `.ail.json` module: it strips `(con T)`
wrappings, drops redundant parentheses, infixes arithmetic, suppresses
schema rigor that the LLM can re-derive. That makes prose pleasant to
read and edit — but it also means there is no syntactic parser that
turns edited prose back into a `.ail.json`.

Re-integration of free-text edits therefore requires an **LLM
mediator**: a model that understands both the prose intent and the
load-bearing detail in the original module, and emits a new module
that respects both. This document describes the workflow, the prompt
template `ail merge-prose` composes for that mediator, and the failure
modes to watch for.

## What the LLM produces

Iter 20f revised a load-bearing piece of this cycle: the LLM emits
**Form-A** (an `.ail` document — the canonical authoring surface
fixed by Decision 6), not JSON-AST. JSON-AST is the canonical
hashable artefact, but it is not a writing surface — every type
reference wraps in `{"k": "con", ...}`, every term in
`{"t": "...", ...}`, and a single missing `param_modes` entry is a
schema error rather than a parse error with a position.

Form-A is the form `examples/*.ail` are written in, the form
`ail render` produces, and the form `ail parse` consumes. The full
LLM-targeted specification is shipped inside the binary as
`ailang_core::FORM_A_SPEC` (sourced from
`crates/ailang-core/specs/form_a.md`) and embedded verbatim in every
`merge-prose` prompt; the LLM does not need prior AILang exposure.

## The cycle

```
1.  ail prose foo.ail.json > foo.prose.txt
2.  $EDITOR foo.prose.txt                     # human edits freely
3.  ail merge-prose foo.ail.json foo.prose.txt > prompt.txt
4.  cat prompt.txt | <your-llm-cli> > foo.new.ail
5.  ail parse foo.new.ail > foo.new.ail.json
6.  ail check foo.new.ail.json
7.  mv foo.new.ail.json foo.ail.json          # if check is clean
```

Step 1 is the deterministic projection (Iter 20a / 20b).
Step 3 is the prompt composer this iter ships. The prompt embeds the
Form-A spec, the original module rendered as Form-A, and the edited
prose. The original module is loaded via `ailang_core::load_module`
(which validates the schema) and rendered via
`ailang_surface::print`.
Step 4 is the user's external LLM client — Claude Code, the
Anthropic API, OpenAI's CLI, anything that takes a prompt on stdin and
emits text on stdout. AILang ships no client of its own; see *Why no
built-in API client* below.
Step 5 parses the LLM's Form-A back into a JSON-AST. Parser errors
are positional and can be fed back to the LLM in a follow-up prompt.
Step 6 typechecks. The full diagnostic catalogue applies — mode
violations, exhaustiveness, effect closure, etc.
Step 7 accepts the new module if check is clean.

## The prompt template

`ail merge-prose <original.ail.json> <edited.prose.txt>` reads the
original (parsing it, then re-rendering as Form-A), reads the prose
file byte-for-byte, and prints the following structure to stdout —
no fences, no JSON envelope. The shape is exactly what a human would
compose by hand if they wanted to drive the same cycle without the
CLI helper.

```
You are integrating prose edits back into an AILang module.

ROLE
Your job is to produce an updated AILang module in Form-A (an .ail
file) that reflects the human's prose edits while preserving the
load-bearing semantic detail from the original module.

CONTRACT
The prose is the source of intent ... [list of preservation rules]

OUTPUT
Output ONLY the new Form-A bytes. ... [no fences, no commentary]

FORM-A SPECIFICATION
<<<FORM_A_SPEC
[ailang_core::FORM_A_SPEC, verbatim]
FORM_A_SPEC

ORIGINAL MODULE (Form-A)
<<<ORIGINAL_FORM_A
[ailang_surface::print(&original_module)]
ORIGINAL_FORM_A

EDITED PROSE
<<<EDITED_PROSE
[edited prose, verbatim]
EDITED_PROSE

Emit the updated Form-A module now.
```

The three payloads (spec, original Form-A, edited prose) are inserted
between heredoc-style markers. `merge-prose` does not strip,
re-encode, or otherwise transform the prose or the spec. The original
is parsed and re-rendered (which is lossless for any module that has
been through `ail parse` / `ail render` already; round-trip is a
gating contract on the surface crate).

## Failure modes

The mediator is an LLM, so the cycle is intentionally iterative.
Common failure modes and their fix:

  - **Output wrapped in markdown fences (` ```ailang ... ``` `).**
    Strip the fences and re-run `ail parse`. If the LLM does this
    consistently, paste a corrective note ("emit raw Form-A, no
    fences") at the top of the next prompt.
  - **Output contains commentary before/after the module.** Same
    treatment. Most modern LLMs respect "OUTPUT ONLY" but not all.
  - **`ail parse` rejects the output.** The parser emits positional
    errors. Re-run with the parse error pasted into the prompt as a
    corrective note — "your previous output failed at byte N with
    `expected X, got Y`; fix that and re-emit".
  - **Parse-clean but `ail check` fails.** Re-run with the
    diagnostic messages pasted in. Common cases on first-time LLM
    use: missing mode annotation on a `(fn ...)` def
    (`mode-required-on-fn-param`), `(app)` used where `(term-ctor)`
    was needed, missing `(effects IO)` on a fn that calls `(do io/...)`.
  - **Preserved detail dropped (mode flipped, doc string lost,
    suppress clause stripped).** Re-run with a corrective note
    naming the missing detail. The contract section calls these out
    explicitly, but a long edit may push the LLM to re-derive
    rather than preserve.

The cycle converges in 1–2 rounds for typical edits with a
modern frontier model.

## Why no built-in API client

AILang stays a compiler + tooling. Shipping an HTTP client to one
particular LLM vendor would mean an API key story, rate limiting,
streaming semantics, error mapping, version tracking — all carrying
zero language-level value. The user already has Claude Code, the
Anthropic SDK, the OpenAI CLI, `curl`, or any other client they
prefer; `ail merge-prose` composes the prompt and gets out of the
way. The Unix-pipe shape (`ail merge-prose ... | client | ail parse |
ail check`) makes the cycle scriptable without lock-in to one vendor.

A future iter may layer a tool-use schema (LLM calls back into
`ail parse` / `ail check` from inside its turn) or an MCP server
(any MCP-compatible client discovers AILang's resources, tools, and
prompts) on top of this cycle. Both are additive — the static-prompt
fallback documented here remains the lowest-common-denominator path
that always works.
